Applying neural network language models to weighted finite state transducers for automatic speech recognition

ABSTRACT

Systems and processes for converting speech-to-text are provided. In one example process, speech input can be received. A sequence of states and arcs of a weighted finite state transducer (WFST) can be traversed. A negating finite state transducer (FST) can be traversed. A virtual FST can be composed using a neural network language model and based on the sequence of states and arcs of the WFST. The one or more virtual states of the virtual FST can be traversed to determine a probability of a candidate word given one or more history candidate words. Text corresponding to the speech input can be determined based on the probability of the candidate word given the one or more history candidate words. An output can be provided based on the text corresponding to the speech input.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Ser. No.62/262,286, filed on Dec. 2, 2015, entitled APPLYING NEURAL NETWORKLANGUAGE MODELS TO WEIGHTED FINITE STATE TRANSDUCERS FOR AUTOMATICSPEECH RECOGNITION, which is hereby incorporated by reference in itsentirety for all purposes. This application also relates to thefollowing co-pending applications: U.S. Non-Provisional patentapplication Ser. No. 14/494,305, “METHOD FOR SUPPORTING DYNAMIC GRAMMARSIN WFST-BASED ASR,” filed Sep. 23, 2014 (Attorney Docket No.106842107900 (P22210US1)), which is hereby incorporated by reference inits entirety for all purposes.

FIELD

The present disclosure relates generally to speech-to-text conversion,and more specifically to techniques for applying neural network languagemodels to weighted finite state transducers for automatic speechrecognition.

BACKGROUND

Language models can be implemented in automatic speech recognition (ASR)to predict the most probable current word (w) given one or more historywords (h). Conventionally, statistical language models, such as n-gramlanguage models, are applied in automatic speech recognition.Statistical language models are based on estimating conditionalprobabilities (e.g., probability of the current word given the one ormore history words, P(w|h)) using training data, such as corpora oftext. In order to achieve high recognition accuracy, the length ofhistory words can be between two to four words (e.g., 3-gram to 5-gram).As the amount of language training data used in modern ASR systems isvery large, the number of n-grams in n-gram language models can be verylarge. Large numbers of n-grams pose memory and speed problems inrun-time ASR systems. Techniques such as pruning and cut-off have beenimplemented to control the actual number of n-grams in an n-gramlanguage model. However, pruning and cut-off can reduce the accuracy ofspeech recognition.

BRIEF SUMMARY

Systems and processes for converting speech-to-text are provided. In oneexample process, speech input can be received. A sequence of states andarcs of a weighted finite state transducer (WFST) can be traversed basedon the speech input. The sequence of states and arcs can represent oneor more history candidate words and a current candidate word. A firstprobability of the candidate word given the one or more historycandidate words can be determined by traversing the sequences of statesand arcs of the WFST. A negating finite state transducer (FST) can betraversed, where traversing the negating FST can negate the firstprobability of the candidate word given the one or more historycandidate words. A virtual FST can be composed using a neural networklanguage model and based on the sequence of states and arcs of the WFST.One or more virtual states of the virtual FST can represent the currentcandidate word. The one or more virtual states of the virtual FST can betraversed, where a second probability of the candidate word given theone or more history candidate words is determined by traversing the oneor more virtual states of the virtual FST. Text corresponding to thespeech input can be determined based on the second probability of thecandidate word given the one or more history candidate words. An outputcan be provided based on the text corresponding to the speech input.

BRIEF DESCRIPTION OF THE FIGURES

For a better understanding of the various described embodiments,reference should be made to the Description of Embodiments below, inconjunction with the following drawings in which like reference numeralsrefer to corresponding parts throughout the figures.

FIG. 1A is a block diagram illustrating a portable multifunction devicewith a touch-sensitive display in accordance with some examples.

FIG. 1B is a block diagram illustrating exemplary components for eventhandling in accordance with some embodiments.

FIG. 2 illustrates a portable multifunction device having a touch screenin accordance with some embodiments.

FIG. 3 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface in accordance with someembodiments.

FIGS. 4A and 4B illustrate an exemplary user interface for a menu ofapplications on a portable multifunction device in accordance with someembodiments.

FIG. 5 illustrates an exemplary schematic block diagram of an automaticspeech recognition module in accordance with some embodiments.

FIG. 6 illustrates an exemplary neural network language model inaccordance with some embodiments.

FIGS. 7A-B illustrate flow diagrams of an exemplary process forspeech-to-text conversion in accordance with some embodiments.

FIGS. 8A-C illustrate flow diagrams of an exemplary process forspeech-to-text conversion in accordance with some embodiments.

FIG. 9 illustrates a functional block diagram of an electronic device inaccordance with some embodiments.

DESCRIPTION OF EMBODIMENTS

In the following description of the disclosure and embodiments,reference is made to the accompanying drawings in which it is shown byway of illustration of specific embodiments that can be practiced. It isto be understood that other embodiments and examples can be practicedand changes can be made without departing from the scope of thedisclosure.

Techniques for applying neural network language models to weightedfinite state transducers for automatic speech recognition are describedherein. Neural network language models (NNLMs) map word indices to acontinuous space and word probability distributions are estimated assmooth functions in that space. As a result, compared to n-gram languagemodels, NNLMs provide better generalization for n-grams that are notfound or are infrequently found in the training data. This enablesgreater recognition accuracy when NNLMs are implemented in automaticspeech recognition. However, because NNLMs are generally configured tomodel the unabridged n-gram (e.g., for feedforward NNLMs) or the entireword history (e.g., for recurrent NNLMs), it can be difficult toefficiently integrate NNLMs into single pass WFST speech recognitiondecoder systems. In particular, NNLMs implemented in automatic speechrecognition can be computationally expensive.

One approach to improving computational efficiency can be to firstconvert the NNLM into an intermediate form, such as an n-gramrepresentation or a prefix tree representation of word sequences. Theintermediate form can then be pruned or optimized before beingintegrated into the single pass WFST. However, the conversion processcan require applying approximations, which can reduce the overallaccuracy of the speech recognition system. The benefits of the NNLM arethus not fully experienced using this approach. Another approach toimproving computation efficiency can be to implement a two-pass strategywhere an n-gram language model is utilized to guide the initial decodingin the WFST. The NNLM can then be utilized during a second pass only toresolve ambiguities. However, the two-pass strategy can result inincreased latency, which can negatively impact user experience forreal-time applications. Further, because the NNLM is only utilized toresolve ambiguities, the benefits associated with the NNLM are notrealized for every speech recognition pass.

Systems and processes for applying neural network language models toweighted finite state transducers for automatic speech recognition aredescribed below. The exemplary systems and processes described hereincan efficiently integrate an NNLM in a single pass WFST withoutsacrificing accuracy. In particular, the NNLM can be directly integratedwith the WFST without converting the NNLM into an intermediate form(e.g., an n-gram or prefix tree representation). Further, the NNLM canbe utilized during every decoding pass, rather than only during therescoring pass in the latency time.

Embodiments of electronic devices, systems for speech-to-text conversionon such devices, and associated processes for using such devices aredescribed. In some embodiments, the device is a portable communicationsdevice, such as a mobile telephone, that also contains other functions,such as PDA and/or music player functions. Exemplary embodiments ofportable multifunction devices include, without limitation, the iPhone®,iPod Touch®, and iPad® devices from Apple Inc. of Cupertino, Calif.Other portable devices, such as laptops or tablet computers withtouch-sensitive surfaces (e.g., touch screen displays and/or touchpads), may also be used. Exemplary embodiments of laptop and tabletcomputers include, without limitation, the iPad® and MacBook® devicesfrom Apple Inc. of Cupertino, Calif. It should also be understood that,in some embodiments, the device is not a portable communications device,but is a desktop computer. Exemplary embodiments of desktop computersinclude, without limitation, the Mac Pro® from Apple Inc. of Cupertino,Calif.

In the discussion that follows, an electronic device that includes adisplay and a touch-sensitive surface is described. It should beunderstood, however, that the electronic device optionally includes oneor more other physical user-interface devices, such as button(s), aphysical keyboard, a mouse, and/or a joystick.

The device may support a variety of applications, such as one or more ofthe following: a drawing application, a presentation application, a wordprocessing application, a website creation application, a disk authoringapplication, a spreadsheet application, a gaming application, atelephone application, a video conferencing application, an e-mailapplication, an instant messaging application, a workout supportapplication, a photo management application, a digital cameraapplication, a digital video camera application, a web browsingapplication, a digital music player application, and/or a digital videoplayer application.

The various applications that are executed on the device optionally useat least one common physical user-interface device, such as thetouch-sensitive surface. One or more functions of the touch-sensitivesurface as well as corresponding information displayed on the deviceare, optionally, adjusted and/or varied from one application to the nextand/or within a respective application. In this way, a common physicalarchitecture (such as the touch-sensitive surface) of the deviceoptionally supports the variety of applications with user interfacesthat are intuitive and transparent to the user.

FIGS. 1A and 1B are block diagrams illustrating exemplary portablemultifunction device 100 with touch-sensitive displays 112 in accordancewith some embodiments. Touch-sensitive display 112 is sometimes called a“touch screen” for convenience. Device 100 may include memory 102.Device 100 may include memory controller 122, one or more processingunits (CPU's) 120, peripherals interface 118, RF circuitry 108, audiocircuitry 110, speaker 111, microphone 113, input/output (I/O) subsystem106, other input or control devices 116, and external port 124. Device100 may include one or more optical sensors 164. Bus/signal lines 103may allow these components to communicate with one another. Device 100is one example of an electronic device that could be used to perform thetechniques described herein. Specific implementations involving device100 may have more or fewer components than shown, may combine two ormore components, or may have a different configuration or arrangement ofthe components. The various components shown in FIGS. 1A and 1B may beimplemented in hardware, software, or a combination of both. Thecomponents also can be implemented using one or more signal processingand/or application specific integrated circuits.

Memory 102 may include one or more computer readable storage mediums.The computer readable storage mediums may be tangible andnon-transitory. Further, one or more computer readable storage mediumsmay include instructions for performing any of the methods or processesdescribed herein. Memory 102 may include high-speed random access memoryand may also include non-volatile memory, such as one or more magneticdisk storage devices, flash memory devices, or other non-volatilesolid-state memory devices. Memory controller 122 may control access tomemory 102 by other components of device 100.

Peripherals interface 118 can be used to couple input and outputperipherals of the device to CPU 120 and memory 102. The one or moreprocessors 120 run or execute various software programs and/or sets ofinstructions stored in memory 102 to perform various functions fordevice 100 and to process data. In some embodiments, peripheralsinterface 118, CPU 120, and memory controller 122 may be implemented ona single chip, such as chip 104. In some other embodiments, they may beimplemented on separate chips.

RF (radio frequency) circuitry 108 receives and sends RF signals, alsocalled electromagnetic signals. RF circuitry 108 converts electricalsignals to/from electromagnetic signals and communicates withcommunications networks and other communications devices via theelectromagnetic signals. RF circuitry 108 may include well-knowncircuitry for performing these functions, including but not limited toan antenna system, an RF transceiver, one or more amplifiers, a tuner,one or more oscillators, a digital signal processor, a CODEC chipset, asubscriber identity module (SIM) card, memory, and so forth. RFcircuitry 108 may communicate with networks, such as the Internet, alsoreferred to as the World Wide Web (WWW), an intranet and/or a wirelessnetwork, such as a cellular telephone network, a wireless local areanetwork (LAN) and/or a metropolitan area network (MAN), and otherdevices by wireless communication. The wireless communication may useany of a plurality of communications standards, protocols andtechnologies, including but not limited to Global System for MobileCommunications (GSM), Enhanced Data GSM Environment (EDGE), high-speeddownlink packet access (HSDPA), wideband code division multiple access(W-CDMA), code division multiple access (CDMA), time division multipleaccess (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity(Wi-Fi) (e.g., IEEE 502.11a, IEEE 502.11b, IEEE 802.11g and/or IEEE802.11n), voice over Internet Protocol (VoIP), Wi-MAX, a protocol fore-mail (e.g., Internet message access protocol (IMAP) and/or post officeprotocol (POP)), instant messaging (e.g., extensible messaging andpresence protocol (XMPP), Session Initiation Protocol for InstantMessaging and Presence Leveraging Extensions (SIMPLE), Instant Messagingand Presence Service (IMPS)), and/or Short Message Service (SMS), or anyother suitable communication protocol, including communication protocolsnot yet developed as of the filing date of this document.

Audio circuitry 110, speaker 111, and microphone 113 provide an audiointerface between a user and device 100. Audio circuitry 110 receivesaudio data from peripherals interface 118, converts the audio data to anelectrical signal, and transmits the electrical signal to speaker 111.Speaker 111 converts the electrical signal to human-audible sound waves.Audio circuitry 110 also receives electrical signals converted bymicrophone 113 from sound waves. Audio circuitry 110 converts theelectrical signal to audio data and transmits the audio data toperipherals interface 118 for processing. Audio data may be retrievedfrom and/or transmitted to memory 102 and/or RF circuitry 108 byperipherals interface 118. In some embodiments, audio circuitry 110 alsoincludes a headset jack (e.g., 212, FIG. 2). The headset jack providesan interface between audio circuitry 110 and removable audioinput/output peripherals, such as output-only headphones or a headsetwith both output (e.g., a headphone for one or both ears) and input(e.g., a microphone).

I/O subsystem 106 couples input/output peripherals on device 100, suchas touch screen 112 and other input control devices 116, to peripheralsinterface 118. I/O subsystem 106 may include display controller 156 andone or more input controllers 160 for other input or control devices.The one or more input controllers 160 receive/send electrical signalsfrom/to other input or control devices 116. The other input controldevices 116 may include physical buttons (e.g., push buttons, rockerbuttons, etc.), dials, slider switches, joysticks, click wheels, and soforth. In some alternate embodiments, input controller(s) 160 may becoupled to any (or none) of the following: a keyboard, infrared port,USB port, and a pointer device such as a mouse. The one or more buttons(e.g., 208, FIG. 2) may include an up/down button for volume control ofspeaker 111 and/or microphone 113. The one or more buttons may include apush button (e.g., 206, FIG. 2). A quick press of the push button maydisengage a lock of touch screen 112 or begin a process that usesgestures on the touch screen to unlock the device, as described in U.S.patent application Ser. No. 11/322,549, “Unlocking a Device byPerforming Gestures on an Unlock Image,” filed Dec. 23, 2005, U.S. Pat.No. 7,657,849, which is hereby incorporated by reference in itsentirety. A longer press of the push button (e.g., 206) may turn powerto device 100 on or off. The user may be able to customize afunctionality of one or more of the buttons. Touch screen 112 is used toimplement virtual or soft buttons and one or more soft keyboards.

Touch-sensitive display 112 provides an input interface and an outputinterface between the device and a user. Display controller 156 receivesand/or sends electrical signals from/to touch screen 112. Touch screen112 displays visual output to the user. The visual output may includegraphics, text, icons, video, and any combination thereof (collectivelytermed “graphics”). In some embodiments, some or all of the visualoutput may correspond to user-interface objects.

Touch screen 112 has a touch-sensitive surface, sensor or set of sensorsthat accepts input from the user based on haptic and/or tactile contact.Touch screen 112 and display controller 156 (along with any associatedmodules and/or sets of instructions in memory 102) detect contact (andany movement or breaking of the contact) on touch screen 112 andconverts the detected contact into interaction with user-interfaceobjects (e.g., one or more soft keys, icons, web-pages or images) thatare displayed on touch screen 112. In an exemplary embodiment, a pointof contact between touch screen 112 and the user corresponds to a fingerof the user.

Touch screen 112 may use LCD (liquid crystal display) technology, LPD(light emitting polymer display) technology, or LED (light emittingdiode) technology, although other display technologies may be used inother embodiments. Touch screen 112 and display controller 156 maydetect contact and any movement or breaking thereof using any of aplurality of touch sensing technologies now known or later developed,including but not limited to capacitive, resistive, infrared, andsurface acoustic wave technologies, as well as other proximity sensorarrays or other elements for determining one or more points of contactwith touch screen 112. In an exemplary embodiment, projected mutualcapacitance sensing technology is used, such as that found in theiPhone® and iPod Touch® from Apple Inc. of Cupertino, Calif.

A touch-sensitive display in some embodiments of touch screen 112 may beanalogous to the multi-touch sensitive touchpads described in thefollowing U.S. Pat. No. 6,323,846 (Westerman et al.), U.S. Pat. No.6,570,557 (Westerman et al.), and/or U.S. Pat. No. 6,677,932(Westerman), and/or U.S. Patent Publication 2002/0015024A1, each ofwhich is hereby incorporated by reference in its entirety. However,touch screen 112 displays visual output from device 100, whereas touchsensitive touchpads do not provide visual output.

A touch-sensitive display in some embodiments of touch screen 112 may beas described in the following applications: (1) U.S. patent applicationSer. No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2,2006; (2) U.S. patent application Ser. No. 10/840,862, “MultipointTouchscreen,” filed May 6, 2004; (3) U.S. patent application Ser. No.10/903,964, “Gestures For Touch Sensitive Input Devices,” filed Jul. 30,2004; (4) U.S. patent application Ser. No. 11/048,264, “Gestures ForTouch Sensitive Input Devices,” filed Jan. 31, 2005; (5) U.S. patentapplication Ser. No. 11/038,590, “Mode-Based Graphical User InterfacesFor Touch Sensitive Input Devices,” filed Jan. 18, 2005; (6) U.S. patentapplication Ser. No. 11/228,758, “Virtual Input Device Placement On ATouch Screen User Interface,” filed Sep. 16, 2005; (7) U.S. patentapplication Ser. No. 11/228,700, “Operation Of A Computer With A TouchScreen Interface,” filed Sep. 16, 2005; (8) U.S. patent application Ser.No. 11/228,737, “Activating Virtual Keys Of A Touch-Screen VirtualKeyboard,” filed Sep. 16, 2005; and (9) U.S. patent application Ser. No.11/367,749, “Multi-Functional Hand-Held Device,” filed Mar. 3, 2006. Allof these applications are incorporated by reference herein in theirentirety.

Touch screen 112 may have a video resolution in excess of 100 dpi. Insome embodiments, the touch screen has a video resolution ofapproximately 160 dpi. The user may make contact with touch screen 112using any suitable object or appendage, such as a stylus, a finger, andso forth. In some embodiments, the user interface is designed to workprimarily with finger-based contacts and gestures, which can be lessprecise than stylus-based input due to the larger area of contact of afinger on the touch screen. In some embodiments, the device translatesthe rough finger-based input into a precise pointer/cursor position orcommand for performing the actions desired by the user.

In some embodiments, in addition to the touch screen, device 100 mayinclude a touchpad (not shown) for activating or deactivating particularfunctions. In some embodiments, the touchpad is a touch-sensitive areaof the device that, unlike the touch screen, does not display visualoutput. The touchpad may be a touch-sensitive surface that is separatefrom touch screen 112 or an extension of the touch-sensitive surfaceformed by the touch screen.

Device 100 also includes power system 162 for powering the variouscomponents. Power system 162 may include a power management system, oneor more power sources (e.g., battery, alternating current (AC)), arecharging system, a power failure detection circuit, a power converteror inverter, a power status indicator (e.g., a light-emitting diode(LED)) and any other components associated with the generation,management and distribution of power in portable devices.

Device 100 may also include one or more optical sensors 164. FIGS. 1Aand 1B show an optical sensor coupled to optical sensor controller 158in I/O subsystem 106. Optical sensor 164 may include charge-coupleddevice (CCD) or complementary metal-oxide semiconductor (CMOS)phototransistors. Optical sensor 164 receives light from theenvironment, projected through one or more lens, and converts the lightto data representing an image. In conjunction with imaging module 143(also called a camera module), optical sensor 164 may capture stillimages or video. In some embodiments, an optical sensor is located onthe back of device 100, opposite touch screen display 112 on the frontof the device, so that the touch screen display may be used as aviewfinder for still and/or video image acquisition. In someembodiments, an optical sensor is located on the front of the device sothat the user's image may be obtained for videoconferencing while theuser views the other video conference participants on the touch screendisplay. In some embodiments, the position of optical sensor 164 can bechanged by the user (e.g., by rotating the lens and the sensor in thedevice housing) so that a single optical sensor 164 may be used alongwith the touch screen display for both video conferencing and stilland/or video image acquisition.

Device 100 may also include one or more proximity sensors 166. FIGS. 1Aand 1B show proximity sensor 166 coupled to peripherals interface 118.Alternately, proximity sensor 166 may be coupled to input controller 160in I/O subsystem 106. Proximity sensor 166 may perform as described inU.S. patent application Ser. No. 11/241,839, “Proximity Detector InHandheld Device”; Ser. No. 11/240,788, “Proximity Detector In HandheldDevice”; Ser. No. 11/620,702, “Using Ambient Light Sensor To AugmentProximity Sensor Output”; Ser. No. 11/586,862, “Automated Response ToAnd Sensing Of User Activity In Portable Devices”; and Ser. No.11/638,251, “Methods And Systems For Automatic Configuration OfPeripherals,” which are hereby incorporated by reference in theirentirety. In some embodiments, the proximity sensor turns off anddisables touch screen 112 when the multifunction device is placed nearthe user's ear (e.g., when the user is making a phone call).

Device 100 optionally also includes one or more tactile outputgenerators 167. FIG. 1A shows a tactile output generator coupled tohaptic feedback controller 161 in I/O subsystem 106. Tactile outputgenerator 167 optionally includes one or more electroacoustic devicessuch as speakers or other audio components and/or electromechanicaldevices that convert energy into linear motion such as a motor,solenoid, electroactive polymer, piezoelectric actuator, electrostaticactuator, or other tactile output generating component (e.g., acomponent that converts electrical signals into tactile outputs on thedevice). Contact intensity sensor 165 receives tactile feedbackgeneration instructions from haptic feedback module 133 and generatestactile outputs on device 100 that are capable of being sensed by a userof device 100. In some embodiments, at least one tactile outputgenerator is collocated with, or proximate to, a touch-sensitive surface(e.g., touch-sensitive display system 112) and, optionally, generates atactile output by moving the touch-sensitive surface vertically (e.g.,in/out of a surface of device 100) or laterally (e.g., back and forth inthe same plane as a surface of device 100). In some embodiments, atleast one tactile output generator sensor is located on the back ofdevice 100, opposite touch screen display 112, which is located on thefront of device 100.

Device 100 may also include one or more accelerometers 168. FIGS. 1A and1B show accelerometer 168 coupled to peripherals interface 118.Alternately, accelerometer 168 may be coupled to an input controller 160in I/O subsystem 106. Accelerometer 168 may perform as described in U.S.Patent Publication No. 20050190059, “Acceleration-based Theft DetectionSystem for Portable Electronic Devices,” and U.S. Patent Publication No.20060017692, “Methods And Apparatuses For Operating A Portable DeviceBased On An Accelerometer,” both of which are which are incorporated byreference herein in their entirety. In some embodiments, information isdisplayed on the touch screen display in a portrait view or a landscapeview based on an analysis of data received from the one or moreaccelerometers. Device 100 optionally includes, in addition toaccelerometer(s) 168, a magnetometer (not shown) and a GPS (or GLONASSor other global navigation system) receiver (not shown) for obtaininginformation concerning the location and orientation (e.g., portrait orlandscape) of device 100.

In some embodiments, the software components stored in memory 102include operating system 126, communication module (or set ofinstructions) 128, contact/motion module (or set of instructions) 130,graphics module (or set of instructions) 132, text input module (or setof instructions) 134, Global Positioning System (GPS) module (or set ofinstructions) 135, and applications (or sets of instructions) 136.Furthermore, in some embodiments memory 102 stores device/globalinternal state 157, as shown in FIGS. 1A, 1B and 3. Device/globalinternal state 157 includes one or more of: active application state,indicating which applications, if any, are currently active; displaystate, indicating what applications, views or other information occupyvarious regions of touch screen display 112; sensor state, includinginformation obtained from the device's various sensors and input controldevices 116; and location information concerning the device's locationand/or attitude.

Operating system 126 (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS,WINDOWS, or an embedded operating system such as VxWorks) includesvarious software components and/or drivers for controlling and managinggeneral system tasks (e.g., memory management, storage device control,power management, etc.) and facilitates communication between varioushardware and software components.

Communication module 128 facilitates communication with other devicesover one or more external ports 124 and also includes various softwarecomponents for handling data received by RF circuitry 108 and/orexternal port 124. External port 124 (e.g., Universal Serial Bus (USB),FIREWIRE, etc.) is adapted for coupling directly to other devices orindirectly over a network (e.g., the Internet, wireless LAN, etc.). Insome embodiments, the external port is a multi-pin connector that is thesame as, or similar to and/or compatible with the 5-pin and/or 30-pinconnectors used on devices made by Apple Inc.

Contact/motion module 130 may detect contact with touch screen 112 (inconjunction with display controller 156) and other touch sensitivedevices (e.g., a touchpad or physical click wheel). Contact/motionmodule 130 includes various software components for performing variousoperations related to detection of contact, such as determining ifcontact has occurred (e.g., detecting a finger-down event), determiningif there is movement of the contact and tracking the movement across thetouch-sensitive surface (e.g., detecting one or more finger-draggingevents), and determining if the contact has ceased (e.g., detecting afinger-up event or a break in contact). Contact/motion module 130receives contact data from the touch-sensitive surface. Determiningmovement of the point of contact, which is represented by a series ofcontact data, may include determining speed (magnitude), velocity(magnitude and direction), and/or an acceleration (a change in magnitudeand/or direction) of the point of contact. These operations may beapplied to single contacts (e.g., one finger contacts) or to multiplesimultaneous contacts (e.g., “multitouch”/multiple finger contacts). Insome embodiments, contact/motion module 130 and display controller 156detects contact on a touchpad. In some embodiments, contact/motionmodule 130 and controller 160 detects contact on a click wheel.

Contact/motion module 130 may detect a gesture input by a user.Different gestures on the touch-sensitive surface have different contactpatterns. Thus, a gesture may be detected by detecting a particularcontact pattern. For example, detecting a finger tap gesture includesdetecting a finger-down event followed by detecting a finger-up (liftoff) event at the same position (or substantially the same position) asthe finger-down event (e.g., at the position of an icon). As anotherexample, detecting a finger swipe gesture on the touch-sensitive surfaceincludes detecting a finger-down event followed by detecting one or morefinger-dragging events, and subsequently followed by detecting afinger-up (lift off) event.

Graphics module 132 includes various known software components forrendering and displaying graphics on touch screen 112 or other display,including components for changing the intensity of graphics that aredisplayed. As used herein, the term “graphics” includes any object thatcan be displayed to a user, including without limitation text,web-pages, icons (such as user-interface objects including soft keys),digital images, videos, animations and the like. In some embodiments,graphics module 132 stores data representing graphics to be used. Eachgraphic may be assigned a corresponding code. Graphics module 132receives, from applications etc., one or more codes specifying graphicsto be displayed along with, if necessary, coordinate data and othergraphic property data, and then generates screen image data to output todisplay controller 156.

Haptic feedback module 133 includes various software components forgenerating instructions used by tactile output generator(s) 167 toproduce tactile outputs at one or more locations on device 100 inresponse to user interactions with device 100.

Text input module 134, which may be a component of graphics module 132,provides soft keyboards for entering text in various applications (e.g.,contacts 137, e-mail 140, IM 141, browser 147, and any other applicationthat needs text input).

GPS module 135 determines the location of the device and provides thisinformation for use in various applications (e.g., to telephone 138 foruse in location-based dialing, to camera 143 as picture/video metadata,and to applications that provide location-based services such as weatherwidgets, local yellow page widgets, and map/navigation widgets).

Applications 136 may include the following modules (or sets ofinstructions), or a subset or superset thereof:

-   -   Contacts module 137 (sometimes called an address book or contact        list);    -   Telephone module 138;    -   Video conferencing module 139;    -   E-mail client module 140;    -   Instant messaging (IM) module 141;    -   Workout support module 142;    -   Camera module 143 for still and/or video images;    -   Image management module 144;    -   Video player module;    -   Music player module;    -   Browser module 147;    -   Calendar module 148;    -   Widget modules 149, which may include one or more of: weather        widget 149-1, stocks widget 149-2, calculator widget 149-3,        alarm clock widget 149-4, dictionary widget 149-5, and other        widgets obtained by the user, as well as user-created widgets        149-6;    -   Widget creator module 150 for making user-created widgets 149-6;    -   Search module 151;    -   Video and music player module 152, which merges video player        module and music player module;    -   Notes module 153;    -   Map module 154; and/or    -   Online video module 155.

Examples of other applications 136 that may be stored in memory 102include other word processing applications, other image editingapplications, drawing applications, presentation applications,JAVA-enabled applications, encryption, digital rights management, voicerecognition, and voice replication.

In conjunction with touch screen 112, display controller 156,contact/motion module 130, graphics module 132, and text input module134, contacts module 137 may be used to manage an address book orcontact list (e.g., stored in application internal state 192 of contactsmodule 137 in memory 102 or memory 370), including: adding name(s) tothe address book; deleting name(s) from the address book; associatingtelephone number(s), e-mail address(es), physical address(es) or otherinformation with a name; associating an image with a name; categorizingand sorting names; providing telephone numbers or e-mail addresses toinitiate and/or facilitate communications by telephone 138, videoconference module 139, e-mail 140, or IM 141; and so forth.

In conjunction with RF circuitry 108, audio circuitry 110, speaker 111,microphone 113, touch screen 112, display controller 156, contact/motionmodule 130, graphics module 132, and text input module 134, telephonemodule 138 may be used to enter a sequence of characters correspondingto a telephone number, access one or more telephone numbers in addressbook 137, modify a telephone number that has been entered, dial arespective telephone number, conduct a conversation and disconnect orhang up when the conversation is completed. As noted above, the wirelesscommunication may use any of a plurality of communications standards,protocols and technologies.

In conjunction with RF circuitry 108, audio circuitry 110, speaker 111,microphone 113, touch screen 112, display controller 156, optical sensor164, optical sensor controller 158, contact module 130, graphics module132, text input module 134, contacts module 137, and telephone module138, video conference module 139 includes executable instructions toinitiate, conduct, and terminate a video conference between a user andone or more other participants in accordance with user instructions.

In conjunction with RF circuitry 108, touch screen 112, displaycontroller 156, contact/motion module 130, graphics module 132, and textinput module 134, e-mail client module 140 includes executableinstructions to create, send, receive, and manage e-mail in response touser instructions. In conjunction with image management module 144,e-mail client module 140 makes it very easy to create and send e-mailswith still or video images taken with camera module 143.

In conjunction with RF circuitry 108, touch screen 112, displaycontroller 156, contact module 130, graphics module 132, and text inputmodule 134, the instant messaging module 141 includes executableinstructions to enter a sequence of characters corresponding to aninstant message, to modify previously entered characters, to transmit arespective instant message (for example, using a Short Message Service(SMS) or Multimedia Message Service (MMS) protocol for telephony-basedinstant messages or using XMPP, SIMPLE, or IMPS for Internet-basedinstant messages), to receive instant messages and to view receivedinstant messages. In some embodiments, transmitted and/or receivedinstant messages may include graphics, photos, audio files, video filesand/or other attachments as are supported in a MMS and/or an EnhancedMessaging Service (EMS). As used herein, “instant messaging” refers toboth telephony-based messages (e.g., messages sent using SMS or MMS) andInternet-based messages (e.g., messages sent using XMPP, SIMPLE, orIMPS).

In conjunction with RF circuitry 108, touch screen 112, displaycontroller 156, contact module 130, graphics module 132, text inputmodule 134, GPS module 135, map module 154, and music player module,workout support module 142 includes executable instructions to createworkouts (e.g., with time, distance, and/or calorie burning goals);communicate with workout sensors (sports devices); receive workoutsensor data; calibrate sensors used to monitor a workout; select andplay music for a workout; and display, store and transmit workout data.

In conjunction with touch screen 112, display controller 156, opticalsensor(s) 164, optical sensor controller 158, contact/motion module 130,graphics module 132, and image management module 144, camera module 143includes executable instructions to capture still images or video(including a video stream) and store them into memory 102, modifycharacteristics of a still image or video, or delete a still image orvideo from memory 102.

In conjunction with touch screen 112, display controller 156,contact/motion module 130, graphics module 132, text input module 134,and camera module 143, image management module 144 includes executableinstructions to arrange, modify (e.g., edit), or otherwise manipulate,label, delete, present (e.g., in a digital slide show or album), andstore still and/or video images.

In conjunction with touch screen 112, display controller 156,contact/motion module 130, graphics module 132, audio circuitry 110, andspeaker 111, video player module 145 includes executable instructions todisplay, present or otherwise play back videos (e.g., on touch screen112 or on an external, connected display via external port 124).

In conjunction with touch screen 112, display system controller 156,contact module 130, graphics module 132, audio circuitry 110, speaker111, RF circuitry 108, and browser module 147, music player module 146includes executable instructions that allow the user to download andplay back recorded music and other sound files stored in one or morefile formats, such as MP3 or AAC files. In some embodiments, device 100may include the functionality of an MP3 player, such as an iPod(trademark of Apple Inc.).

In conjunction with RF circuitry 108, touch screen 112, displaycontroller 156, contact/motion module 130, graphics module 132, and textinput module 134, browser module 147 includes executable instructions tobrowse the Internet in accordance with user instructions, includingsearching, linking to, receiving, and displaying web-pages or portionsthereof, as well as attachments and other files linked to web-pages.

In conjunction with RF circuitry 108, touch screen 112, displaycontroller 156, contact/motion module 130, graphics module 132, textinput module 134, e-mail client module 140, and browser module 147,calendar module 148 includes executable instructions to create, display,modify, and store calendars and data associated with calendars (e.g.,calendar entries, to do lists, etc.) in accordance with userinstructions.

In conjunction with RF circuitry 108, touch screen 112, displaycontroller 156, contact/motion module 130, graphics module 132, textinput module 134, and browser module 147, widget modules 149 aremini-applications that may be downloaded and used by a user (e.g.,weather widget 149-1, stocks widget 149-2, calculator widget 149-3,alarm clock widget 149-4, and dictionary widget 149-5) or created by theuser (e.g., user-created widget 149-6). In some embodiments, a widgetincludes an HTML (Hypertext Markup Language) file, a CSS (CascadingStyle Sheets) file, and a JavaScript file. In some embodiments, a widgetincludes an XML (Extensible Markup Language) file and a JavaScript file(e.g., Yahoo! Widgets).

In conjunction with RF circuitry 108, touch screen 112, displaycontroller 156, contact/motion module 130, graphics module 132, textinput module 134, and browser module 147, the widget creator module 150may be used by a user to create widgets (e.g., turning a user-specifiedportion of a web-page into a widget).

In conjunction with touch screen 112, display controller 156,contact/motion module 130, graphics module 132, and text input module134, search module 151 includes executable instructions to search fortext, music, sound, image, video, and/or other files in memory 102 thatmatch one or more search criteria (e.g., one or more user-specifiedsearch terms) in accordance with user instructions.

In conjunction with touch screen 112, display controller 156,contact/motion module 130, graphics module 132, audio circuitry 110,speaker 111, RF circuitry 108, and browser module 147, video and musicplayer module 152 includes executable instructions that allow the userto download and play back recorded music and other sound files stored inone or more file formats, such as MP3 or AAC files, and executableinstructions to display, present, or otherwise play back videos (e.g.,on touch screen 112 or on an external, connected display via externalport 124). In some embodiments, device 100 optionally includes thefunctionality of an MP3 player, such as an iPod (trademark of AppleInc.).

In conjunction with touch screen 112, display controller 156,contact/motion module 130, graphics module 132, and text input module134, notes module 153 includes executable instructions to create andmanage notes, to-do lists, and the like in accordance with userinstructions.

In conjunction with RF circuitry 108, touch screen 112, displaycontroller 156, contact/motion module 130, graphics module 132, textinput module 134, GPS module 135, and browser module 147, map module 154may be used to receive, display, modify, and store maps and dataassociated with maps (e.g., driving directions; data on stores and otherpoints of interest at or near a particular location; and otherlocation-based data) in accordance with user instructions.

In conjunction with touch screen 112, display controller 156,contact/motion module 130, graphics module 132, audio circuitry 110,speaker 111, RF circuitry 108, text input module 134, e-mail clientmodule 140, and browser module 147, online video module 155 includesinstructions that allow the user to access, browse, receive (e.g., bystreaming and/or download), play back (e.g., on the touch screen or onan external, connected display via external port 124), send an e-mailwith a link to a particular online video, and otherwise manage onlinevideos in one or more file formats, such as H.264. In some embodiments,instant messaging module 141, rather than e-mail client module 140, isused to send a link to a particular online video. Additional descriptionof the online video application can be found in U.S. Provisional PatentApplication No. 60/936,562, “Portable Multifunction Device, Method, andGraphical User Interface for Playing Online Videos,” filed Jun. 20,2007, and U.S. patent application Ser. No. 11/968,067, “PortableMultifunction Device, Method, and Graphical User Interface for PlayingOnline Videos,” filed Dec. 31, 2007, the contents of which are herebyincorporated by reference in their entirety.

Each of the above identified modules and applications corresponds to aset of executable instructions for performing one or more functionsdescribed above and the methods described in this application (e.g., thecomputer-implemented methods and other information processing methodsdescribed herein). These modules (e.g., sets of instructions) need notbe implemented as separate software programs, procedures or modules, andthus various subsets of these modules may be combined or otherwiserearranged in various embodiments. For example, video player module maybe combined with music player module into a single module (e.g., videoand music player module 152, FIG. 1B). In some embodiments, memory 102may store a subset of the modules and data structures identified above.Furthermore, memory 102 may store additional modules and data structuresnot described above.

In some embodiments, memory 102 (or memory 370 of FIG. 3) may storevarious user-specific data, such as, for example, user-specificvocabulary data, preference data, data from the user's electronicaddress book, user generated to-do lists, user generated shopping lists,or the like.

In some embodiments, device 100 is a device where operation of apredefined set of functions on the device is performed exclusivelythrough a touch screen and/or a touchpad. By using a touch screen and/ora touchpad as the primary input control device for operation of device100, the number of physical input control devices (such as push buttons,dials, and the like) on device 100 may be reduced.

The predefined set of functions that may be performed exclusivelythrough a touch screen and/or a touchpad include navigation between userinterfaces. In some embodiments, the touchpad, when touched by the user,navigates device 100 to a main, home, or root menu from any userinterface that may be displayed on device 100. In such embodiments, a“menu button” is implemented using a touchpad. In some otherembodiments, the menu button is a physical push button or other physicalinput control device instead of a touchpad.

FIG. 1B is a block diagram illustrating exemplary components for eventhandling in accordance with some embodiments. In some embodiments,memory 102 (in FIG. 1A) or 370 (FIG. 3) includes event sorter 170 (e.g.,in operating system 126) and a respective application 136-1 (e.g., anyof the aforementioned applications 137-151, 155, 380-390).

Event sorter 170 receives event information and determines theapplication 136-1 and application view 191 of application 136-1 to whichto deliver the event information. Event sorter 170 includes eventmonitor 171 and event dispatcher module 174. In some embodiments,application 136-1 includes application internal state 192, whichindicates the current application view(s) displayed on touch sensitivedisplay 112 when the application is active or executing. In someembodiments, device/global internal state 157 is used by event sorter170 to determine which application(s) is(are) currently active, andapplication internal state 192 is used by event sorter 170 to determineapplication views 191 to which to deliver event information.

In some embodiments, application internal state 192 includes additionalinformation, such as one or more of: resume information to be used whenapplication 136-1 resumes execution, user interface state informationthat indicates information being displayed or that is ready for displayby application 136-1, a state queue for enabling the user to go back toa prior state or view of application 136-1, and a redo/undo queue ofprevious actions taken by the user.

Event monitor 171 receives event information from peripherals interface118. Event information includes information about a sub-event (e.g., auser touch on touch-sensitive display 112, as part of a multi-touchgesture). Peripherals interface 118 transmits information it receivesfrom I/O subsystem 106 or a sensor, such as proximity sensor 166,accelerometer(s) 168, and/or microphone 113 (through audio circuitry110). Information that peripherals interface 118 receives from I/Osubsystem 106 includes information from touch-sensitive display 112 or atouch-sensitive surface.

In some embodiments, event monitor 171 sends requests to the peripheralsinterface 118 at predetermined intervals. In response, peripheralsinterface 118 transmits event information. In other embodiments,peripherals interface 118 transmits event information only when there isa significant event (e.g., receiving an input above a predeterminednoise threshold and/or for more than a predetermined duration). In someembodiments, event sorter 170 also includes a hit view determinationmodule 172 and/or an active event recognizer determination module 173.

Hit view determination module 172 provides software procedures fordetermining where a sub-event has taken place within one or more views,when touch sensitive display 112 displays more than one view. Views aremade up of controls and other elements that a user can see on thedisplay.

Another aspect of the user interface associated with an application is aset of views, sometimes herein called application views or userinterface windows, in which information is displayed and touch-basedgestures occur. The application views (of a respective application) inwhich a touch is detected may correspond to programmatic levels within aprogrammatic or view hierarchy of the application. For example, thelowest level view in which a touch is detected may be called the hitview, and the set of events that are recognized as proper inputs may bedetermined based, at least in part, on the hit view of the initial touchthat begins a touch-based gesture.

Hit view determination module 172 receives information related tosub-events of a touch-based gesture. When an application has multipleviews organized in a hierarchy, hit view determination module 172identifies a hit view as the lowest view in the hierarchy which shouldhandle the sub-event. In most circumstances, the hit view is the lowestlevel view in which an initiating sub-event occurs (e.g., the firstsub-event in the sequence of sub-events that form an event or potentialevent). Once the hit view is identified by the hit view determinationmodule 172, the hit view typically receives all sub-events related tothe same touch or input source for which it was identified as the hitview.

Active event recognizer determination module 173 determines which viewor views within a view hierarchy should receive a particular sequence ofsub-events. In some embodiments, active event recognizer determinationmodule 173 determines that only the hit view should receive a particularsequence of sub-events. In other embodiments, active event recognizerdetermination module 173 determines that all views that include thephysical location of a sub-event are actively involved views, andtherefore determines that all actively involved views should receive aparticular sequence of sub-events. In other embodiments, even if touchsub-events were entirely confined to the area associated with oneparticular view, views higher in the hierarchy would still remain asactively involved views.

Event dispatcher module 174 dispatches the event information to an eventrecognizer (e.g., event recognizer 180). In embodiments including activeevent recognizer determination module 173, event dispatcher module 174delivers the event information to an event recognizer determined byactive event recognizer determination module 173. In some embodiments,event dispatcher module 174 stores in an event queue the eventinformation, which is retrieved by a respective event receiver 182.

In some embodiments, operating system 126 includes event sorter 170.Alternatively, application 136-1 includes event sorter 170. In yet otherembodiments, event sorter 170 is a stand-alone module, or a part ofanother module stored in memory 102, such as contact/motion module 130.

In some embodiments, application 136-1 includes a plurality of eventhandlers 190 and one or more application views 191, each of whichincludes instructions for handling touch events that occur within arespective view of the application's user interface. Each applicationview 191 of the application 136-1 includes one or more event recognizers180. Typically, a respective application view 191 includes a pluralityof event recognizers 180. In other embodiments, one or more of eventrecognizers 180 are part of a separate module, such as a user interfacekit (not shown) or a higher level object from which application 136-1inherits methods and other properties. In some embodiments, a respectiveevent handler 190 includes one or more of: data updater 176, objectupdater 177, GUI updater 178, and/or event data 179 received from eventsorter 170. Event handler 190 may utilize or call data updater 176,object updater 177, or GUI updater 178 to update the applicationinternal state 192. Alternatively, one or more of the application views191 include one or more respective event handlers 190. Also, in someembodiments, one or more of data updater 176, object updater 177, andGUI updater 178 are included in a respective application view 191.

A respective event recognizer 180 receives event information (e.g.,event data 179) from event sorter 170 and identifies an event from theevent information. Event recognizer 180 includes event receiver 182 andevent comparator 184. In some embodiments, event recognizer 180 alsoincludes at least a subset of: metadata 183, and event deliveryinstructions 188 (which may include sub-event delivery instructions).

Event receiver 182 receives event information from event sorter 170. Theevent information includes information about a sub-event, for example, atouch or a touch movement. Depending on the sub-event, the eventinformation also includes additional information, such as location ofthe sub-event. When the sub-event concerns motion of a touch the eventinformation may also include speed and direction of the sub-event. Insome embodiments, events include rotation of the device from oneorientation to another (e.g., from a portrait orientation to a landscapeorientation, or vice versa), and the event information includescorresponding information about the current orientation (also calleddevice attitude) of the device.

Event comparator 184 compares the event information to predefined eventor sub-event definitions and, based on the comparison, determines anevent or sub-event, or determines or updates the state of an event orsub-event. In some embodiments, event comparator 184 includes eventdefinitions 186. Event definitions 186 contain definitions of events(e.g., predefined sequences of sub-events), for example, event 1(187-1), event 2 (187-2), and others. In some embodiments, sub-events inan event (187) include, for example, touch begin, touch end, touchmovement, touch cancellation, and multiple touching. In one example, thedefinition for event 1 (187-1) is a double tap on a displayed object.The double tap, for example, comprises a first touch (touch begin) onthe displayed object for a predetermined phase, a first liftoff (touchend) for a predetermined phase, a second touch (touch begin) on thedisplayed object for a predetermined phase, and a second liftoff (touchend) for a predetermined phase. In another example, the definition forevent 2 (187-2) is a dragging on a displayed object. The dragging, forexample, comprises a touch (or contact) on the displayed object for apredetermined phase, a movement of the touch across touch-sensitivedisplay 112, and liftoff of the touch (touch end). In some embodiments,the event also includes information for one or more associated eventhandlers 190.

In some embodiments, event definitions 187 include a definition of anevent for a respective user-interface object. In some embodiments, eventcomparator 184 performs a hit test to determine which user-interfaceobject is associated with a sub-event. For example, in an applicationview in which three user-interface objects are displayed ontouch-sensitive display 112, when a touch is detected on touch-sensitivedisplay 112, event comparator 184 performs a hit test to determine whichof the three user-interface objects is associated with the touch(sub-event). If each displayed object is associated with a respectiveevent handler 190, the event comparator uses the result of the hit testto determine which event handler 190 should be activated. For example,event comparator 184 selects an event handler associated with thesub-event and the object triggering the hit test.

In some embodiments, the definition for a respective event (187) alsoincludes delayed actions that delay delivery of the event informationuntil after it has been determined whether the sequence of sub-eventsdoes or does not correspond to the event recognizer's event type.

When a respective event recognizer 180 determines that the series ofsub-events do not match any of the events in event definitions 186, therespective event recognizer 180 enters an event impossible, eventfailed, or event ended state, after which it disregards subsequentsub-events of the touch-based gesture. In this situation, other eventrecognizers, if any, that remain active for the hit view continue totrack and process sub-events of an ongoing touch-based gesture.

In some embodiments, a respective event recognizer 180 includes metadata183 with configurable properties, flags, and/or lists that indicate howthe event delivery system should perform sub-event delivery to activelyinvolved event recognizers. In some embodiments, metadata 183 includesconfigurable properties, flags, and/or lists that indicate how eventrecognizers may interact, or are enabled to interact, with one another.In some embodiments, metadata 183 includes configurable properties,flags, and/or lists that indicate whether sub-events are delivered tovarying levels in the view or programmatic hierarchy.

In some embodiments, a respective event recognizer 180 activates eventhandler 190 associated with an event when one or more particularsub-events of an event are recognized. In some embodiments, a respectiveevent recognizer 180 delivers event information associated with theevent to event handler 190. Activating an event handler 190 is distinctfrom sending (and deferred sending) sub-events to a respective hit view.In some embodiments, event recognizer 180 throws a flag associated withthe recognized event, and event handler 190 associated with the flagcatches the flag and performs a predefined process.

In some embodiments, event delivery instructions 188 include sub-eventdelivery instructions that deliver event information about a sub-eventwithout activating an event handler. Instead, the sub-event deliveryinstructions deliver event information to event handlers associated withthe series of sub-events or to actively involved views. Event handlersassociated with the series of sub-events or with actively involved viewsreceive the event information and perform a predetermined process.

In some embodiments, data updater 176 creates and updates data used inapplication 136-1. For example, data updater 176 updates the telephonenumber used in contacts module 137, or stores a video file used in videoplayer module. In some embodiments, object updater 177 creates andupdates objects used in application 136-1. For example, object updater177 creates a new user-interface object or updates the position of auser-interface object. GUI updater 178 updates the GUI. For example, GUIupdater 178 prepares display information and sends it to graphics module132 for display on a touch-sensitive display.

In some embodiments, event handler(s) 190 includes or has access to dataupdater 176, object updater 177, and GUI updater 178. In someembodiments, data updater 176, object updater 177, and GUI updater 178are included in a single module of a respective application 136-1 orapplication view 191. In other embodiments, they are included in two ormore software modules.

It shall be understood that the foregoing discussion regarding eventhandling of user touches on touch-sensitive displays also applies toother forms of user inputs to operate multifunction devices 100 withinput devices, not all of which are initiated on touch screens. Forexample, mouse movement and mouse button presses, optionally coordinatedwith single or multiple keyboard presses or holds; contact movementssuch as taps, drags, scrolls, etc. on touchpads; pen stylus inputs;movement of the device; oral instructions; detected eye movements;biometric inputs; and/or any combination thereof are optionally utilizedas inputs corresponding to sub-events which define an event to berecognized.

FIG. 2 illustrates a portable multifunction device 100 having a touchscreen 112 in accordance with some embodiments. The touch screen maydisplay one or more graphics within user interface (UI) 200. In thisembodiment, as well as others described below, a user may select one ormore of the graphics by making contact or touching the graphics, forexample, with one or more fingers 202 (not drawn to scale in the figure)or one or more styluses 203 (not drawn to scale in the figure). In someembodiments, selection of one or more graphics occurs when the userbreaks contact with the one or more graphics. In some embodiments, thecontact may include a gesture, such as one or more taps, one or moreswipes (from left to right, right to left, upward and/or downward)and/or a rolling of a finger (from right to left, left to right, upwardand/or downward) that has made contact with device 100. In someembodiments, inadvertent contact with a graphic may not select thegraphic. For example, a swipe gesture that sweeps over an applicationicon may not select the corresponding application when the gesturecorresponding to selection is a tap.

Device 100 may also include one or more physical buttons, such as “home”or menu button 204. As described previously, menu button 204 may be usedto navigate to any application 136 in a set of applications that may beexecuted on device 100. Alternatively, in some embodiments, the menubutton is implemented as a soft key in a GUI displayed on touch screen112.

In one embodiment, device 100 includes touch screen 112, menu button204, push button 206 for powering the device on/off and locking thedevice, volume adjustment button(s) 208, Subscriber Identity Module(SIM) card slot 210, head set jack 212, and docking/charging externalport 124. Push button 206 may be used to turn the power on/off on thedevice by depressing the button and holding the button in the depressedstate for a predefined time interval; to lock the device by depressingthe button and releasing the button before the predefined time intervalhas elapsed; and/or to unlock the device or initiate an unlock process.In an alternative embodiment, device 100 also may accept verbal inputfor activation or deactivation of some functions through microphone 113.

FIG. 3 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface in accordance with someembodiments. Device 300 need not be portable. In some embodiments,device 300 is a laptop computer, a desktop computer, a tablet computer,a multimedia player device, a navigation device, an educational device(such as a child's learning toy), a gaming system, or a control device(e.g., a home or industrial controller). Device 300 typically includesone or more processing units (CPU's) 310, one or more network or othercommunications interfaces 360, memory 370, and one or more communicationbuses 320 for interconnecting these components. Communication buses 320may include circuitry (sometimes called a chipset) that interconnectsand controls communications between system components. Device 300includes input/output (I/O) interface 330 comprising display 340, whichis typically a touch screen display. I/O interface 330 also may includea keyboard and/or mouse (or other pointing device) 350 and touchpad 355.Memory 370 includes high-speed random access memory, such as DRAM, SRAM,DDR RAM or other random access solid state memory devices; and mayinclude non-volatile memory, such as one or more magnetic disk storagedevices, optical disk storage devices, flash memory devices, or othernon-volatile solid state storage devices. Memory 370 may optionallyinclude one or more storage devices remotely located from CPU(s) 310. Insome embodiments, memory 370 stores programs, modules, and datastructures analogous to the programs, modules, and data structuresstored in memory 102 of portable multifunction device 100 (FIG. 1), or asubset thereof. Furthermore, memory 370 may store additional programs,modules, and data structures not present in memory 102 of portablemultifunction device 100. For example, memory 370 of device 300 maystore drawing module 380, presentation module 382, word processingmodule 384, website creation module 386, disk authoring module 388,and/or spreadsheet module 390, while memory 102 of portablemultifunction device 100 (FIG. 1) may not store these modules.

Each of the above identified elements in FIG. 3 may be stored in one ormore of the previously mentioned memory devices. Each of the aboveidentified modules corresponds to a set of instructions for performing afunction described above. The above identified modules or programs(i.e., sets of instructions) need not be implemented as separatesoftware programs, procedures or modules, and thus various subsets ofthese modules may be combined or otherwise re-arranged in variousembodiments. In some embodiments, memory 370 may store a subset of themodules and data structures identified above. Furthermore, memory 370may store additional modules and data structures not described above.

Attention is now directed towards embodiments of user interfaces (“UI”)that may be implemented on portable multifunction device 100. FIG. 4Aillustrates exemplary user interfaces for a menu of applications onportable multifunction device 100 in accordance with some embodiments.Similar user interfaces may be implemented on device 300. In someembodiments, user interface 400 includes the following elements, or asubset or superset thereof:

-   -   Signal strength indicator(s) 402 for wireless communication(s),        such as cellular and Wi-Fi signals;    -   Time 404;    -   Bluetooth indicator 405;    -   Battery status indicator 406;    -   Tray 408 with icons for frequently used applications, such as:        -   Icon 416 for telephone module 138, labeled “Phone,” which            optionally includes an indicator 414 of the number of missed            calls or voicemail messages;        -   Icon 418 for e-mail client module 140, labeled “Mail,” which            optionally includes an indicator 410 of the number of unread            e-mails;        -   Icon 420 for browser module 147, labeled “Browser;” and        -   Icon 422 for video and music player module 152, also            referred to as iPod (trademark of Apple Inc.) module 152,            labeled “iPod;” and    -   Icons for other applications, such as:        -   Icon 424 for IM module 141, labeled “Messages;”        -   Icon 426 for calendar module 148, labeled “Calendar;”        -   Icon 428 for image management module 144, labeled “Photos;”        -   Icon 430 for camera module 143, labeled “Camera;”        -   Icon 432 for online video module 155, labeled “Online            Video;”        -   Icon 434 for stocks widget 149-2, labeled “Stocks;”        -   Icon 436 for map module 154, labeled “Maps;”        -   Icon 438 for weather widget 149-1, labeled “Weather;”        -   Icon 440 for alarm clock widget 149-4, labeled “Clock;”        -   Icon 442 for workout support module 142, labeled “Workout            Support;”        -   Icon 444 for notes module 153, labeled “Notes;” and        -   Icon 446 for a settings application or module, labeled            “Settings,” which provides access to settings for device 100            and its various applications 136.

FIG. 4B illustrates an exemplary user interface on a device (e.g.,device 300, FIG. 3) with a touch-sensitive surface 451 (e.g., a tabletor touchpad 355, FIG. 3) that is separate from the display 450 (e.g.,touch screen display 112). Although many of the examples which followwill be given with reference to inputs on touch screen display 112(where the touch sensitive surface and the display are combined), insome embodiments, the device detects inputs on a touch-sensitive surfacethat is separate from the display, as shown in FIG. 4B. In someembodiments the touch sensitive surface (e.g., 451) has a primary axis(e.g., 452) that corresponds to a primary axis (e.g., 453) on thedisplay (e.g., 450). In accordance with these embodiments, the devicedetects contacts (e.g., 460 and 462) with the touch-sensitive surface451 at locations that correspond to respective locations on the display(e.g., 460 corresponds to 468 and 462 corresponds to 470). In this way,user inputs (e.g., contacts 460 and 462, and movements thereof) detectedby the device on the touch-sensitive surface (e.g., 451) are used by thedevice to manipulate the user interface on the display (e.g., 450) ofthe multifunction device when the touch-sensitive surface is separatefrom the display. It should be understood that similar methods may beused for other user interfaces described herein.

Additionally, while the following examples are given primarily withreference to finger inputs (e.g., finger contacts, finger tap gestures,finger swipe gestures), it should be understood that, in someembodiments, one or more of the finger inputs are replaced with inputfrom another input device (e.g., a mouse-based input or stylus input).For example, a swipe gesture is, optionally, replaced with a mouse click(e.g., instead of a contact) followed by movement of the cursor alongthe path of the swipe (e.g., instead of movement of the contact). Asanother example, a tap gesture is, optionally, replaced with a mouseclick while the cursor is located over the location of the tap gesture(e.g., instead of detection of the contact followed by ceasing to detectthe contact). Similarly, when multiple user inputs are simultaneouslydetected, it should be understood that multiple computer mice are,optionally, used simultaneously, or a mouse and finger contacts are,optionally, used simultaneously.

As used in the specification and claims, the term “open application”refers to a software application with retained state information (e.g.,as part of device/global internal state 157 and/or application internalstate 192). An open (e.g., executing) application is any one of thefollowing types of applications:

-   -   an active application, which is currently displayed on display        112 (or a corresponding application view is currently displayed        on the display);    -   a background application (or background process), which is not        currently displayed on display 112, but one or more application        processes (e.g., instructions) for the corresponding application        are being processed by one or more processors 120 (i.e.,        running);    -   a suspended application, which is not currently running, and the        application is stored in a volatile memory (e.g., DRAM, SRAM,        DDR RAM, or other volatile random access solid state memory        device of memory 102); and    -   a hibernated application, which is not running, and the        application is stored in a non-volatile memory (e.g., one or        more magnetic disk storage devices, optical disk storage        devices, flash memory devices, or other non-volatile solid state        storage devices of memory 102).

As used herein, the term “closed application” refers to softwareapplications without retained state information (e.g., state informationfor closed applications is not stored in a memory of the device).Accordingly, closing an application includes stopping and/or removingapplication processes for the application and removing state informationfor the application from the memory of the device. Generally, opening asecond application while in a first application does not close the firstapplication. When the second application is displayed and the firstapplication ceases to be displayed, the first application becomes abackground application.

FIG. 5 illustrates an exemplary schematic block diagram of ASR module500 in accordance with some embodiments. In some embodiments, ASR module500 can be implemented using one or more multifunction devices,including but not limited to devices 100, 400, and 900 (FIGS. 1A, 2,4A-B, and 9). The multifunctional devices can include devices such asservers, personal computers, mobile device, or the like. In particular,ASR module 500 can be implemented in the memory (e.g., memory 102 or370) and/or processor(s) (e.g., processor(s) 120 or 310) of one or moredevices. ASR module 500 can enable speech recognition capabilities in amultifunctional device. In particular, ASR module 500 can be configuredto perform any of the processes or methods described below (e.g.,processes 700 and 800).

As shown in FIG. 5, ASR module 500 can include feature extractor 502configured to process speech input and extract acoustic features fromthe speech input. In particular, feature extractor 502 can divide thespeech input into a plurality of speech frames, each speech frame havinga predetermined duration (e.g., 10 ms). Feature extractor 502 canfurther be configured to extract acoustic features (e.g., mel-frequencycepstral coefficients, etc.) from the plurality of audio frames. Theacoustic features can be referred to as feature vectors. The acousticfeatures can represent various acoustic qualities of the speech input.

The extracted acoustic features can be received by recognition engine504, which can utilize one or more FSTs and/or language models toconvert the acoustic features into text. In particular, recognitionengine 504 can use WFST 506, negating FST 508, n-gram language model510, user-specific language model FST(s) 512, virtual FST interface 514,or NNLM 516 to convert the acoustic features into text.

WFST 506 can be a single optimized finite state transducer composed fromvarious knowledge sources. In particular, WFST 506 can be composed froma Hidden Markov Model (HMM) topology, a context dependent phoneticmodel, a lexicon, and a language model. The combination of theseknowledge sources can be optimized using conventional WFST optimizationtechniques, such as composition, determinization, or minimization.Decoding with WFST 506 using recognition engine 504 can thus be veryefficient as a result of this optimization. In some examples, WFST 506can be denoted as:

HCLG _(s)=opt(H◯C◯L◯G _(s))

where H is an HMM topology transducer, C is a context dependent phoneticmodel transducer, L is a lexicon tranducer, G_(s) is a language modeltransducer, and ◯ denotes the composition operation. The optimizationoperator opt( ) can perform, for example, epsilon-removal, weightpushing, composition, determinization, and minimization. In someexamples, G_(s) can be an n-gram language model. In particular, G_(s)can be a limited sized n-gram language model in order for HCLG_(s) to bea compact and statistically optimized transducer. In some examples,G_(s) can be a unigram or a bigram language model.

In some examples, WFST 506 can be configured to model a non-terminalclass as a candidate word. As described in greater detail below, anon-terminal class can be a class of words (e.g., an entity class suchas names of persons, places, applications, media, etc.). In someexamples, the non-terminal class can be derived from user-specific data(e.g., names in a user's contact list). WFST 506 can be configured tomodel any number of non-terminal classes.

Negating FST 508 can have the same structure and topology as thelanguage model transducer G_(s) from which WFST 506 is built, exceptthat the scores (e.g., costs, likelihoods, or probabilities) arenegated. Negating FST 508 can be denoted as G_(s) ⁻¹. Negating FST 508can be structured such that composing negating FST 508 with WFST 506(HCLG_(s)) would negate the scores associated with the language modeltransducer G_(s). For example, if the weights of G_(s) are logprobabilities, the weights of G_(s) are subtracted from HCLG_(s) whencomposing negating FST 508 with WFST 506. Recognition engine 504 can beconfigured to compose the negating FST 508 with WFST 506.

N-gram language model FST 510 can be a large vocabulary language model.In particular, n-gram language model FST 510 can have a greater numberof n-grams than the language model G_(s) used to generate WFST 506.N-gram language model FST 510 can be used by recognition engine 504 tosupplement WFST 506, thereby obtaining more accurate recognitionresults.

User-specific language model FST(s) 512 can include one or more languagemodel FSTs that are generated using user-specific data. In particular,ASR module includes language model generator for generatinguser-specific language model FST(s) 512 using user-specific data.Language model generator 513 can be configured to receive or obtainuser-specific data (e.g., block 802, described below), such as userinput, user usage data, user profile information, or the like. Languagemodel generator 513 can further be configured to generate one or moreuser-specific language model FSTs 512 using the user-specific data(e.g., block 804, described below). The one or more user-specificlanguage model FSTs 512 can thus contain vocabulary and word sequencesthat are associated with a specific user of the electronic device. Eachuser-specific language model FST 512 can represent a particularnon-terminal class. A non-terminal class can be a class of words.Examples of non-terminal classes can include names in a user's contactlist ($ContactList) on the electronic device, names of applications($AppList) in a user's electronic device, or names of places ($Place”)entered by a user on the electronic device.

Virtual FST interface 514 can be an interface for on-the-flyconstruction of one or more virtual FST using NNLM 516 and n-gramlanguage model FST 510. In particular, virtual FST interface 514 canencode the sequence of states and arcs traversed in WFST 506 byrecognition engine 504. Based on the sequence of states and arcs,virtual FST interface 514 can be configured to provide one or morehistory candidate words (h) to NNLM 516 or n-gram language model 510 andobtain a probability of a current candidate word given the one or morehistory candidate words (P(w|h)). Virtual FST interface 514 can thusenable NNLM 516 and/or n-gram language model FST 510 to be integratedwith WFST 506 during run-time speech recognition.

NNLM 516 can be a multiple layer perceptron. FIG. 6 illustratesexemplary neural NNLM 600 that can be similar or identical to NNLM 516.NNLM 600 can include input layer 602, output layer 604, and one or morehidden layers 606 disposed between input layer 602 and output layer 604.In this example, NNLM 600 includes three hidden layers 606. It should berecognized, however, that in other examples, NNLM 600 can include anynumber of hidden layers 606.

Each layer of NNLM 600 can include multiple units. The units can be thebasic computational elements of NNLM 600 and can be referred to asdimensions, neurons, or nodes. As shown in FIG. 6, input layer 602 caninclude input units 608, hidden layers 606 can include hidden units 610,and output layer 604 can include output units 612. Hidden layers 806 caneach include any number of hidden units 810. The units can beinterconnected by connections 614. Specifically, connections 614 canconnect the units of one layer to the units of a subsequent layer.Further, each connection 614 can be associated with a weighting valueand a bias followed by a nonlinear activation function. For simplicity,the weighting values and biases are not shown in FIG. 6.

Input layer 602 can represent a vocabulary table that maps one or morehistory candidate words (h) in a continuous space where each word isrepresented as a floating point vector. Input layer 602 can beconfigured to receive as inputs one or more history candidate words (h).In the present example, the one or more history candidate words includea first history word (w₁) and a second history word (w₂). Output layer604 can be configured to estimate a probability distribution over theword to predict. In the present example, output layer 604 can beconfigured to output the probability of a third word (w₃) given thefirst history word (w₁) and the second history word (w₂). The number ofoutput units 612 in output layer 604 can have the same number of neuronsas the vocabulary size of NNLM 600. Thus, output layer 604 can beconfigured to output a plurality of probabilities of numerous candidatewords given the word history. It should be recognized that NNLM 600 canbe a feedforward NNLM or a recurrent NNLM.

With reference back to FIG. 5, recognition engine 504 can function as adecoder. In particular, recognition engine 504 can perform decodingfunctions such as composing, interpolating, or traversing functionsdescribed below in processes 700 and 800. For example, recognitionengine 504 can traverse or compose one or more of WFST 506, negating FST508, n-gram language model 510, and user-specific language model FST(s)512 to obtain speech recognition results. Further, recognition engine504 can compose one or more virtual FSTs using one or more of WFST 506,negating FST 508, n-gram language model 510, user-specific languagemodel FST(s) 512, virtual FST interface 514, and NNLM 516. Recognitionengine 504 can then traverse the one or more virtual FSTs to obtain theprobability of a current candidate word given one or more historycandidate words (P(w|h)).

FIGS. 7A-B and 8A-C illustrate flow diagrams of exemplary processes 700and 800 for speech-to-text conversion in accordance with someembodiments. More specifically, processes 700 and 800 can apply a neuralnetwork language model to weighted finite state transducers forspeech-to-text conversion. Processes 700 or 800 can be performed at oneor more of devices 100, 300, and 900 (FIGS. 1A, 2, 3A-B, and 9),described herein. In particular, processes 700 or 800 can be performedusing an ASR module (e.g., ASR module 500 of FIG. 5) implemented on oneor more devices. It should be appreciated that some blocks in processes700 or 800 can be combined, the order of some blocks can be changed, andsome blocks can be omitted.

At block 702, speech input can be received. The speech input can bereceived via a microphone (e.g., microphone 113) of an electronicdevice. The speech input can be in the form of an acoustic signal or anaudio file. The speech input can include a user utterance, such as avoice command, dictation, request, authentication phrase, or the like.In some examples, the speech input can be pre-processed using a featureextractor (e.g., feature extractor 502) where the speech input isdivided into a plurality of speech frames (e.g., 10 ms speech frames)and acoustic features (e.g., mel-frequency cepstral coefficients, etc.)are extracted from the plurality of segments. The acoustic features arethus a representation of the speech input.

At block 704, a sequence of states and arcs of a WFST can be traversedbased on the speech input (e.g., using the acoustic features of block702). The WFST can be similar or identical to WFST 502 described abovein FIG. 5. As described above, the WFST can be referred to as HCLG_(s).In particular, the WFST can be a single optimized finite statetransducer composed from a Hidden Markov Model (HMM) topology (H), acontext dependent phonetic model (C), a lexicon (L), and a languagemodel (G_(s)). In some examples, the language model (G_(s)) from whichthe WFST is built can be a unigram language model or a bigram languagemodel. The WFST can be a static finite state transducer built prior toreceiving the speech input.

The sequence of states and arcs can represent one or more historycandidate words (h) and a current candidate word (w). In some instances,the one or more history candidate words (h) can be referred to ascontext. In one example, the one or more history candidate words (h) canbe “Let's go” and the current candidate word (w) can be “home.” Bytraversing the sequences of states and arcs of the WFST, a firstprobability of the candidate word given the one or more historycandidate words (P₁(w|h)) can be determined.

At block 706, a negating FST can be composed with the WFST. The negatingFST can be similar or identical to negating FST 508 described above. Inparticular, the negating FST can be a static FST built prior toreceiving the speech input at block 702. The negating FST can have thesame structure as the language model transducer G_(s) from which theWFST is built, except the scores (e.g., costs, likelihoods, orprobabilities) are negated. Composing the negating FST can berepresented as follows:

HCLG _(s) ◯G _(s) ⁻¹

where HCLG_(s) denotes the WFST of block 704, G_(s) ⁻¹ denotes thenegating FST, and ◯ denotes the composition operation. In some examples,block 706 can be performed prior to block 708. Further, in someexamples, block 706 can be performed after block 704.

At block 708, the negating FST (G_(s) ⁻¹) can be traversed. Morespecifically, the negating FST composed with the WFST can be traversed.The negating FST can be traversed after traversing the sequence ofstates and arcs of the WFST at block 704. Traversing the negating FSTcan negate the first probability of the candidate word given the one ormore history candidate words, P₁(w|h). The negating FST can be a staticfinite state transducer built prior to receiving the speech input atblock 702.

At block 710, a second probability of the candidate word given the oneor more history candidate words (P₂(w|h)) can be determined using aneural network language model (NNLM). The NNLM can be similar oridentical to NNLMs 514 or 600, described above. In some examples, theNNLM can be a feedforward NNLM. In other examples, the NNLM can be arecurrent NNLM. The NNLM can be more accurate than the language model(G_(s)) used to build the WFST of block 704. In particular, the NNLM canbe more accurate than the language model (G_(s)) in determining theprobability of a word given a history of words. Further, in someexamples, the NNLM can be more accurate than a higher order (e.g.,4-gram or greater) n-gram language model.

The second probability of the candidate word given the one or morehistory candidate words, P₂(w|h) can be determined using a virtual FSTinterface (e.g., virtual FST interface 514, described above). Thevirtual FST interface can encode the one or more history candidate words(h) and the current candidate word (w) traversed in the WFST. The one ormore history candidate words (h) can be inputted into the NNLM (e.g., atinput layer 602) using the virtual FST interface. Based on the input,the NNLM can output the probabilities of numerous candidate words giventhe one or more history candidate words (e.g., from output layer 604).The second probability of the current candidate word given the one ormore history candidate words (P₂(w|h)) can be obtained from among theoutputted probabilities of numerous candidate words given the one ormore history candidate words.

At block 712, a virtual FST (e.g., virtual FST interface 514) can becomposed using the NNLM of block 710 and based on the sequence of statesand arcs of the WFST of block 704. In particular, the virtual FST can becomposed using the second probability of the candidate word given theone or more history candidate words (P₂(w|h)) determined at block 710.The virtual FST can be a virtual representation of the NNLM. Inparticular, the virtual FST can encode the one or more history candidatewords (h) traversed in the WFST at block 704. Further, the virtual FSTcan include one or more virtual states that represent the currentcandidate word with respect to the one or more history candidate words.Composing the virtual FST can be represented as follows:

HCLG _(s) ◯G _(s) ⁻¹ ◯G ₁ _(_) _(NNLM)

where G₁ _(_) _(NNLM) denotes the virtual FST. In some examples, thevirtual FST can be deterministic where there is only one arctransitioning out of each of the one or more virtual states of thevirtual FST. Further, the virtual FST can be composed on-the-fly, wherethe virtual FST is composed only after the sequence of states and arcsof the WFST are traversed. In particular, the virtual FST may notinclude states representing every candidate word outputting by the NNLM.Rather, the virtual FST can include virtual states representing only thecurrent candidate words obtained from traversing the WFST at block 704.The WFST thus guides the decoding. As a result, the computational andmemory requirements can be reduced, which in turn reduces latency andimproves user experience. Block 710 can be performed prior to block 712.

At block 714, the one or more virtual states of the virtual FST can betraversed. The one or more virtual states of the virtual FST can encodea third probability of the candidate word given the one or more historycandidate words (P₃(w|h)). The third probability of the candidate wordgiven the one or more history candidate words (P₃(w|h)) can bedetermined based on the second probability of the candidate word giventhe one or more history candidate words (P₂(w|h)). For example, thethird probability of the candidate word given the one or more historycandidate words (P₂(w|h)) can be logarithmic representation of thesecond probability of the candidate word given the one or more historycandidate words (P₂(w|h)). By traversing the one or more virtual statesof the virtual FST, the third probability of the candidate word giventhe one or more history candidate words (P₃(w|h)) can be determined.

It should be appreciated that the virtual FST is composed and traversedfor every speech recognition pass. Thus, the NNLM is utilized for everyspeech recognition pass and not merely implemented only for resolvingambiguities from the WFST. Further, it should be recognized that theNNLM is not initially converted into an intermediate form such as ann-gram representation or a prefix tree representation. Rather, thevirtual FST is composed directly using the NNLM via the virtual FSTinterface. This preserves the accuracy advantages associated with theNNLM. At the same time, as described above, not all candidate wordresults from the NNLM are used to compose the virtual FST. Rather, thevirtual FST can include virtual states representing only the currentcandidate words obtained from traversing the WFST at block 704. Thus,process 700 enables the greater accuracy of the NNLM to be leveraged inspeech recognition while limiting the computational requirements byutilizing the candidate word results from the WFST to guide thecomposition of the virtual FST.

At block 716, text corresponding to the speech input can be determinedbased on the third probability of the candidate word given the one ormore history candidate words (P₃(w|h)). In particular, the one or morehistory candidate words (h) and the candidate word (w) can represent onecandidate speech recognition result among a plurality of candidatespeech recognition results. For example, the one or more historycandidate words (h) and the candidate word (w) can represent thecandidate speech recognition result “Let's go home.” The candidatespeech result “Let's go home” can be associated with a probability basedon the third probability of the candidate word given the one or morehistory candidate words (P₃(w|h)). Other candidate speech recognitionresults can include “Let's go to Rome,” “Let's grow hope,” or the like.Each candidate speech recognition result can be associated with aprobability. The plurality of candidate speech recognition results canbe ranked according to their respective probabilities. The textcorresponding to the speech input can be the candidate speechrecognition result with the highest probability.

At block 718, an output can be provided based on the text correspondingto the speech input. For example, if the text corresponding to thespeech input is determined at block 716 to be “Let's go home,” the textcan be displayed on the electronic device implementing process 700. Inanother example, the speech input can be provided as a command to adigital assistant implemented on the electronic device. Based on thetext “Let's go home,” the digital assistant can determine that the userwishes to obtain directions back home. In this example, the output caninclude map instructions on how to get home. It should be recognizedthat various other types of output can be provided based on the textcorresponding to the speech input.

In some examples, the text can be determined using one or moreadditional language models. In these examples, blocks 712 and 714 can berepeated using one or more additional language models. For example,blocks 720-724 of FIG. 7B illustrate additional operations of process700 that can be performed when the text corresponding to the speechinput is determined using one or more additional language models. Blocks720-724 can be performed prior to blocks 716 and 718.

At block 720, a second virtual FST can be composed using a secondlanguage model and based on the sequence of states and arcs. Block 720can be similar to block 712 except that the second language model isdifferent from the NNLM. For example, the second language models can bea large vocabulary n-gram language model or a second NNLM that isdifferent from the NNLM of block 710. The second virtual FST can encodethe one or more history candidate words. Further, the second virtual FSTcan include one or more virtual states that represent the currentcandidate word.

In some examples, the second virtual FST can be deterministic wherethere is only one arc transitioning out of each of the one or morevirtual states of the second virtual FST. Further, the virtual FST canbe composed on-the-fly. In particular, the virtual FST can includevirtual states representing only the current candidate words obtainedfrom traversing the WFST at block 704.

At block 722, the one or more virtual states of the second virtual FSTcan be traversed. The one or more virtual states of the virtual FST canencode a fourth probability of the candidate word given the one or morehistory candidate words (P₄(w|h)). The fourth probability of thecandidate word given the one or more history candidate words can bederived using the second language model. By traversing the one or morevirtual states of the second virtual FST, the fourth probability giventhe one or more history candidate words (P₄(w|h)) can be determined.

At block 724, the second probability of the candidate word given the oneor more history candidate words (P₂(w|h)) and the fourth probability ofthe candidate word given the one or more history candidate words(P₄(w|h)) can be interpolated. The interpolation can be represented asfollows:

G ₁ _(_) _(combined) <−G ₁ _(_) _(NNLM)◯₊ G ₁ _(_) ₂

where G₁ _(_) _(NNLM) denotes the virtual FST, G₁ _(_) ₂ denotes thesecond virtual FST, and ◯₊ denotes the interpolation operation. Acombined probability of the candidate word given the one or more historycandidate words can be determined from the interpolation.

The text corresponding to the speech input at block 714 can bedetermined based on the combined probability of the candidate word giventhe one or more history candidate words. The text can thus be determinedbased on both the second probability of the candidate word given the oneor more history candidate words (P₂(w|h)) and the fourth probability ofthe candidate word given the one or more history candidate words(P₄(w|h)). By determining the text using two language models, theaccuracy of the speech recognition is improved. It should be recognizedthat any number of language models can be utilized for speechrecognition using the above described framework. For example, theinterpolation of block 724 can be performed using any number ofprobabilities determined from any number of language models as follows:

G ₁ <−G ₁ _(_) _(NNLM)◯₊ G ₁ _(_) _(2 . . .) ◯_(+ . . .) G ₁ _(_)_((n-1))◯₊ G ₁ _(_) _(n)

where n is an integer and G₁ _(_) _(n) is the n^(th) virtual FSTcomposed using the n^(th) language model.

With reference to FIGS. 8A-8C, process 800 depicts another exemplaryprocess for speech-to-text conversion. Process 800 can be similar toprocess 700 except that the current candidate word can be modeled as anon-terminal class in the WFST.

At block 802, user-specific data can be received. The user-specific datacan be obtained from the memory (e.g., memory 102 or 370) of theelectronic device 102. In some examples, the user-specific data can beassociated with a particular user profile on the electronic device. Theuser-specific data can include lists of words or word sequencesassociated with the user. In particular, the lists of words or wordsequences can include entities associated with the user. Theuser-specific data can further include interaction frequenciesassociated with the words or word sequences.

In some examples, the user-specific usage data can include names foundin a user's phonebook or contact list. In particular, input containingthe contact information (e.g., names, numbers, addresses, etc.) can bereceived from the user in a variety of circumstances, such as in voicecommands, voice dictation, emails, calls, messages, or the like. In someinstances, the user's contact list can include names that may not bewithin the vocabulary of the automatic speech recognition system (e.g.,ASR module 500). These out-of-vocabulary names can thus be received andused to provide recognition support for such user-specific words.

In some examples, the user-specific data can include names ofapplications on the electronic device (e.g., applications 136 on device100). The names of the applications can be retrieved from the memory ofthe electronic device. Additionally, the names of the applications canbe received from the user. For example, input containing the applicationnames can be received from the user in a variety of circumstances, suchas in voice commands to launch an application, close an application,direct instructions to an application, or the like. A user may alsoinput application names when dictating emails, messages, or the like(e.g., recommending an application to a friend, posting to a socialmedia feed the achievement of a new high score in a gaming application,or the like). In some instances, an application on a user device canhave a name that may not be within the vocabulary of the automaticspeech recognition system. The user-specific data can thus include alist of user applications to provide speech recognition support for suchuser-specific application names.

In some examples, the user-specific data can include names of media onthe electronic device, media accessible to a user, or media otherwiseassociated with a user (e.g., media stored in memory on user device 102,media available via streaming applications, media available via theInternet, media available from cloud storage, media available from asubscription service, etc.). Media names can include song tracks, musicalbum titles, playlist names, genre names, mix names, artist names,radio station names, channel names, video titles, performer names,podcast titles, podcast producer names, or the like. For example, inputcontaining media names can be received from the user in a variety ofcircumstances, such as in voice commands to play a song, play a video,tune to a radio station, play a mix of a particular genre of music, playan album, play an artist's music, or the like. A user may also inputmedia names when dictating messages, searching for media, or the like(e.g., recommending an album to a friend, searching for a new song tobuy, searching for a video clip to play, etc.). In some instances, mediaon a user device or available from other sources can have names that maynot be within the vocabulary of the automatic speech recognition system.A list of media associated with a particular user can thus be receivedand used, as discussed in further detail below, to provide recognitionsupport for such user specific media names.

In some examples, the user-specific data can include informationregarding the frequency of interaction with the various entities. Forexample, the frequency of interaction can reflect the number of times acontact name, application name, or media name is received or selected asinput on the electronic device. The frequency of interaction can includea ranking of entities with which the user interacts the most. Further,favorite lists, speed dial lists, or the like can be used to reflect alikely frequency of interaction between the user and various contacts.It should be understood that the frequency of interaction can berepresented in any of a variety of ways (e.g., probabilities,percentages, rankings, interaction counts, number of interactions over aparticular time period, etc.).

It should be appreciated that user-specific data can include a varietyof other entities associated with a user that can be useful for ensuringspeech recognition accuracy. For example, the user-specific data caninclude the names of locations, restaurants, or favorite foodsassociated with the user. Likewise, a variety of context information orother user-specific details can be received for speech recognitionpurposes. In some examples, such other entities and context informationcan be accompanied by interaction frequency data similar to thatdiscussed above reflecting, for example, the likelihood that aparticular entity will correspond to a user's similar-soundingutterance. Block 802 can be performed prior to receiving the speechinput at block 806.

At block 804, a user-specific language model FST can be generated usingthe user-specific data. The user-specific language model FST can be anon-terminal language model FST that corresponds to a non-terminalclass. In particular, the user-specific language model FST can beconfigured to determine the probability of one or more candidate wordswith respect to all possible candidate word sequences for thenon-terminal class. The non-terminal class can correspond to any classof words. In some examples, the non-terminal class can correspond to aparticular entity type such as, contact names, application names, medianames, places, or the like. In one example, the user-specific data caninclude contact names from the user's contact list in the electronicdevice. In particular, the user-specific data can include the frequencyof occurrence of a contact name in user input with respect to allcontact names in the contact list. In this example, a user-specificlanguage model FST representing the non-terminal class “$ContactList”can be generated using the user-specific data. The user-specificlanguage model FST can thus be configured to determine the probabilityof a particular contact name with respect to all contact names in theuser's contact list. The user-specific language model FST can be astatic FST and can be generated at block 804 prior to receiving thespeech input at block 806.

At block 806, speech input can be received. Block 806 can be similar oridentical to block 702, described above.

At block 808, a sequence of states and arcs of a WFST can be traversedbased on the speech input. Block 808 can be similar to block 704, exceptthat the current candidate word is modeled as a non-terminal class. Thesequence of states and arcs can thus represent one or more historycandidate words (h) and a non-terminal class (NT). In some examples, thenon-terminal class represented by the sequence of states and arcs can bethe non-terminal class corresponding to the user-specific language modelFST generated at block 804. By traversing the sequences of states andarcs of the WFST, a first probability of the non-terminal class giventhe one or more history candidate words (P₁(NT|h)) can be determined.The non-terminal class can be a class type that represents a set ofwords or word sequences. One example of a non-terminal class can be“$FirstName,” which can include a set of words or word sequencescorresponding to the names in the user's contact lists (e.g., Jon, Adam,Mary, Fred, Bob, Mary Jane, etc.). Another example of a non-terminalclass can be “$Country,” which can include a set of words or wordsequences corresponding to different countries (e.g., United States,Mexico, France, Germany, China, Korea, Japan, etc.). In some examples,the non-terminal class can be based on the user-specific data of block802. For example, the non-terminal class can be “$ContactList,” whichcan include the names in the user's contact list.

At block 810, a negating finite state transducer (FST) can be composedwith the WFST. Block 810 can be similar or identical to block 706. Inparticular, the negating FST can be similar or identical to negating FST508, described above. The negating FST can be a static FST built priorto receiving the speech input at block 806. The negating FST can havethe same structure as the language model transducer G_(s) from which theWFST is built, except the scores (e.g., costs, likelihoods, orprobabilities) are negated.

At block 812, a negating FST can be traversed. Block 812 can be similaror identical to block 708. In particular, traversing the negating FSTcan negate the first probability of the non-terminal class given the oneor more history candidate words (P₁(NT|h)). The negating FST can be astatic finite state transducer built prior to receiving the speech inputat block 806.

At block 814, a second probability of the non-terminal class given theone or more history candidate words (P₂(NT|h)) can be determined usingan NNLM. Block 814 can be similar to block 710, described above. Inparticular, the NNLM can be similar or identical to NNLMs 514 or 600,described above. In some examples, the NNLM can be a feedforward NNLM.In other examples, the NNLM can be a recurrent NNLM. The NNLM can bemore accurate than the language model (G_(s)) used to build the WFST ofblock 808. In some examples, the NNLM can be more accurate than a higherorder (e.g., 4-gram or greater) n-gram language model.

At block 816, a user-specific language model FST (G_(NT)) correspondingto the non-terminal class can be traversed. The user-specific languagemodel FST (G_(NT)) can be a non-terminal language model FST. Forexample, the user-specific language model FST (G_(NT)) generated atblock 804 can be traversed. By traversing the user-specific languagemodel FST (G_(NT)), a probability of a current candidate word among aplurality of candidate words represented in the user-specific languagemodel FST can be determined. Each of the plurality of candidate wordsrepresented in the user-specific language model FST can be associatedwith a non-terminal class.

At block 818, a virtual FST (G₁) can be composed using the NNLM of block814 and the user-specific language model FST (G_(NT)) of block 816, andbe based on the sequence of states and arcs of the WFST at block 808. Inparticular, the virtual FST (G₁) can be composed by composing a virtualNNLM FST (G₁ _(_) _(NNLM)) with the user-specific language model FST(G_(NT)) as follows:

G ₁ <−G ₁ _(_) _(NNLM)◯_(·) G _(NT)

where ◯_(·) denotes the composition operation with respect to anon-terminal class. The virtual NNLM FST (G₁ _(_) _(NNLM)) can be avirtual representation of the NNLM and can be composed on-the fly in asimilar manner as described in block 712. In particular, the virtualNNLM FST (G₁ _(_) _(NNLM)) can be composed using the second probabilityof the non-terminal class given the one or more history candidate words(P₂(NT|h)) determined at block 814. The virtual NNLM FST (G₁ _(_)_(NNLM)) can encode the one or more history candidate words (h)traversed in the WFST at block 808. Further, the virtual NNLM FST (G₁_(_) _(NNLM)) can include one or more virtual states that represent thenon-terminal class (NT) with respect to the one or more historycandidate words (h).

One or more virtual states of the virtual FST (G₁) can represent acurrent candidate word (w) corresponding to the non-terminal class (NT).For example, if the non-terminal class represents names in the user'scontact list, the one or more virtual states of the virtual FST (G₁) canrepresent a current candidate word (w), such as “Bob,” “Joe,” or “Mike,”corresponding to a name in the user's contact list. The one or morevirtual states of the virtual FST can be composed using phone-words fromthe WFST and based on the current candidate word (w) represented in theuser-specific language model FST (G_(NT)). Phone-words can be words thatrepresent phones. For example, if the current candidate word representedin the user-specific language model FST is “Mike” with a pronunciationof “M-AI-K,” it can be represented by phone-words “M”, “AI”, and “K.”The WFST can be modified to generate these phone-words and the one ormore virtual states of the virtual FST can be composed using thesephone-words. Further, the virtual FST can be composed using theprobability of the current candidate word (w) among the plurality ofcandidate words represented in the user-specific language model FST(block 816). The virtual FST (G₁) can be deterministic where only onearc transitions out of each virtual state of the one or more virtualstates of the virtual FST.

It should be appreciated that in some examples, more than oneuser-specific language model FST can be implemented. In particular, eachuser-specific language model FST can represent a different non-terminalclass. In these examples, the virtual FST (G₁) can be composed with theuser-specific language model FSTs as follows:

G ₁ <−G ₁ _(_) _(NNLM)◯_(·)(G _(NT1) , . . . G _(NTn))

where n is an integer, and G_(NT1), . . . , G_(NTn) denotes n differentuser-specific language model FSTs.

At block 820, the one or more virtual states of the virtual FST can betraversed. The one or more virtual states of the virtual FST can encodethe probability of the candidate word given the one or more historywords and the non-terminal class (P(w|h,NT). The probability of thecandidate word given the one or more history words and the non-terminalclass (P(w|h,NT) can be based on the second probability of thenon-terminal class given the one or more history candidate words(P₂(NT|h) and the probability of the current candidate word (w) amongthe plurality of candidate words represented in the user-specificlanguage model FST (block 816). By traversing the virtual state of thevirtual FST, a probability of the current candidate word given the oneor more history candidate words and the non-terminal class (P₁(w|h,NT))can be determined.

It should be recognized that in some examples, the one or more virtualstates can represent two or more current candidate words correspondingto the non-terminal class. For example, the non-terminal class“$ContactList” can include names having more than one word (e.g., “MaryJane,” “Bob Jones,” “Joe Black,” or “Mike Jordon Smith”). In theseexamples, the probability of two or more current candidate words giventhe one or more history candidate words and the non-terminal class (P₁(w|h,NT)) can be determined.

At block 822, text corresponding to the speech input can be determinedbased on the probability of the current candidate word given the one ormore history candidate words and the non-terminal class (P₁(w|h,NT)).Block 822 can be similar to block 716, described above.

At block 824, an output based on the text corresponding to the speechinput can be provided. Block 824 can be similar or identical to block718, described above.

It should be recognized that, in some examples, the text can bedetermined using one or more additional language models. In theseexamples, blocks 818 and 820 can be repeated using one or moreadditional language models. For example, blocks 826-830 of FIG. 8Cillustrate additional operations of process 800 that can be performedwhen the text corresponding to the speech input is determined using oneor more additional language models. Blocks 826-830 can be performedprior to blocks 822 and 824.

At block 826, a second virtual FST can be composed using a secondlanguage model and the user-specific language model FST and be based onthe sequence of states and arcs of the WFST. Block 826 can be similar toblock 818, except that a different language model is used. One or morevirtual states of the second virtual FST can represent the currentcandidate word (w) corresponding to the non-terminal class (NT). In someexamples, the second language model can be a second NNLM that isdifferent from the NNLM of block 814. In these examples, the secondvirtual FST (G₁ _(_) ₂) can be composed by composing a virtual NNLM FST(G₁ _(_) _(NNLM2)) with the user-specific language model FST (G_(NT)) asfollows:

G ₁ _(_) ₂ <−G ₁ _(_) _(NNLM2)◯_(·) G _(NT)

where ◯_(·) denotes the composition operation with respect to anon-terminal class. In other examples, the second language model can bea large vocabulary n-gram language model. In particular, the secondlanguage model can be a higher order (e.g., 4-gram or greater) n-gramlanguage model. In these examples, the second virtual FST (G₁ _(_) ₂)can be composed by composing a virtual n-gram FST (G_(n-gram)) with theuser-specific language model FST (G_(NT)) as follows:

G ₁ _(_) ₂ <−G ₁ _(_) _(n-gram) replace G _(NT)

where “replace” denotes the replace FST operation with respect to ann-gram language model FST (G₁ _(_) _(n-gram)).

At block 828, the one or more virtual states of the second virtual FSTcan be traversed. Block 828 can be similar to block 820, describedabove. In particular, the one or more virtual states can represent acurrent candidate word (w) with respect to the one or more historycandidate words (h) and the non-terminal state (NT). The currentcandidate word (w) can be obtained from the user-specific language modelFST. The one or more virtual states can encode the second probability ofthe current candidate word given the one or more history candidate wordsand the non-terminal class (P₂(w|h,NT)). By traversing the one or morevirtual states of the second virtual FST, the second probability of thecurrent candidate word given the one or more history candidate words andthe non-terminal class (P₂(w|h,NT)) can be determined.

At block 830, the probability of the current candidate word given theone or more history candidate words and the non-terminal class(P₁(w|h,NT)) from block 820 and the second probability of the currentcandidate word given the one or more history candidate words and thenon-terminal class (P₂(w|h,NT)) from block 828 can be interpolated. Theinterpolation can be represented as follows:

G ₁ _(_) _(combined) <−G ₁ _(_) _(NNLM)◯₊ G ₁ _(_) ₂

where G₁ _(_) _(NNLM) denotes the virtual FST, G₁ _(_) ₂ denotes thesecond virtual FST, and ◯₊ denotes the interpolation operation. Acombined probability of the candidate word given the one or more historycandidate words can be determined from the interpolation.

The text corresponding to the speech input at block 822 can bedetermined based on the combined probability of the candidate word giventhe one or more history candidate words and the non-terminal class. Thetext is thus determined based on both the probability of the candidateword given the one or more history candidate words and the non-terminalclass (P₁(w|h, NT)) and the second probability of the candidate wordgiven the one or more history candidate words and the non-terminal class(P₂(w|h, NT)). By determining the text using two language models, theaccuracy of the speech recognition is improved. As described above, itshould be recognized that any number of language models can be utilizedfor speech recognition using the above described framework. The combinedprobability can thus be based on any number of language models.

In accordance with some embodiments, FIG. 9 shows a functional blockdiagram of an electronic device 900 configured in accordance with theprinciples of the various described embodiments, including thosedescribed with reference to FIG. 6. The functional blocks of the deviceare, optionally, implemented by hardware, software, or a combination ofhardware and software to carry out the principles of the variousdescribed embodiments. It is understood by persons of skill in the artthat the functional blocks described in FIG. 9 are, optionally, combinedor separated into sub-blocks to implement the principles of the variousdescribed embodiments. Therefore, the description herein optionallysupports any possible combination or separation or further definition ofthe functional blocks described herein.

As shown in FIG. 9, electronic device 900 can include display unit 902configured to display text or user interface objects, and audioreceiving unit 904 configured to receive speech input, and input unit906 configured to receive user-specific data. Electronic device 900 canfurther include processing unit 908 coupled to display unit 902 andaudio receiving unit 904. In some examples, processing unit 908 caninclude traversing unit 910, composing unit 912, determining unit 914,providing unit 916, interpolating unit 918, receiving unit 920, anddetermining unit 916.

In accordance with some embodiments, processing unit 908 is configuredto traverse (e.g., with traversing unit 910), based on the speech input,a sequence of states and arcs of a weighted finite state transducer(WFST). The sequence of states and arcs represents one or more historycandidate words and a current candidate word. A first probability of thecandidate word given the one or more history candidate words isdetermined by traversing the sequences of states and arcs of the WFST.Processing unit 908 is further configured to traverse (e.g., withtraversing unit 910) a negating finite state transducer (FST).Traversing the negating FST negates the first probability of thecandidate word given the one or more history candidate words. Processingunit 908 is further configured to compose (e.g., with composing unit912) a virtual FST using a neural network language model and based onthe sequence of states and arcs of the WFST, where one or more virtualstates of the virtual FST represent the current candidate word.Processing unit 908 is further configured to traverse (e.g., withtraversing unit 910) the one or more virtual states of the virtual FST.A second probability of the candidate word given the one or more historycandidate words is determined by traversing the one or more virtualstates of the virtual FST. Processing unit 908 is further configured todetermine (e.g., with determining unit 914), based on the secondprobability of the candidate word given the one or more historycandidate words, text corresponding to the speech input. Processing unit908 is further configured to provide (e.g., with providing unit 916) anoutput based on the text corresponding to the speech input.

In some examples, the virtual FST is composed after traversing thesequence of states and arcs of the WFST.

In some examples, only one arc transitions out of each virtual state ofthe one or more virtual states of the virtual FST.

In some examples, processing unit 908 is further configured to determine(e.g., with determining unit 914), using the neural network languagemodel, a third probability of the candidate word given the one or morehistory candidate words. The virtual FST is composed using the thirdprobability of the candidate word given the one or more historycandidate words.

In some examples, processing unit 908 is further configured to compose(e.g., with composing unit 912) a second virtual FST using a secondlanguage model and based on the sequence of states and arcs, where oneor more virtual states of the second virtual FST represents the currentcandidate word. Processing unit 908 is further configured to traverse(e.g., with traversing unit 910) the one or more virtual states of thesecond virtual FST, where a fourth probability of the candidate wordgiven the one or more history candidate words is determined bytraversing the one or more virtual states of the second virtual FST, andwhere the text corresponding to the speech input is determined based onthe fourth probability of the candidate word given the one or morehistory candidate words.

In some examples, processing unit 908 is further configured tointerpolate (e.g., with interpolating unit 918) the second probabilityof the candidate word given the one or more history candidate words andthe fourth probability of the candidate word given the one or morehistory candidate words. A combined probability of the candidate wordgiven the one or more history candidate words is determined by theinterpolating. The text corresponding to the speech input is determinedbased on the combined probability of the candidate word given the one ormore history candidate words.

In some examples, the second language model is an n-gram language model.

In some examples, processing unit 908 is further configured to compose(e.g., with composing unit 912) the negating FST with the WFST prior totraversing the negating FST.

In some examples, the virtual FST is composed prior to traversing theone or more virtual states of the virtual FST.

In some examples, the WFST is a static finite state transducer builtprior to receiving the speech input. In some examples, the negating FSTis a static finite state transducer built prior to receiving the speechinput. In some examples, the WFST is a single finite state transducercomposed from a Hidden Markov Model (HMM) topology, a context dependentphonetic model, a lexicon, and a language model. In some examples, thelanguage model is a unigram language model or a bigram language model.In some examples, the neural network language model is more accuratethan the language model. In some examples, the neural network languagemodel is a feedforward neural network language model. In some examples,the neural network language model is a recurrent neural network languagemodel.

In accordance with some embodiments, processing unit 908 is configuredto traverse (e.g., with traversing unit 901), based on the speech input,a sequence of states and arcs of a weighted finite state transducer(WFST). The sequence of states and arcs represents one or more historycandidate words and a non-terminal class and a first probability of thenon-terminal class given the one or more history candidate words isdetermined by traversing the sequences of states and arcs of the WFST.Processing unit 908 is further configured to traverse (e.g., withtraversing unit 910) a negating finite state transducer (FST), wheretraversing the negating FST negates the first probability of thenon-terminal class given the one or more history candidate words.Processing unit 908 is further configured to compose (e.g., withcomposing unit 912) a virtual FST using a neural network language modeland a user-specific language model FST, and based on the sequence ofstates and arcs of the WFST. One or more virtual states of the virtualFST represent a current candidate word corresponding to the non-terminalclass. Processing unit 908 is further configured to traverse (e.g., withtraversing 910) the one or more virtual states of the virtual FST, wherea probability of the current candidate word given the one or morehistory candidate words and the non-terminal class is determined bytraversing the one or more virtual states of the virtual FST. Processingunit 908 is further configured to determine (e.g., with determining unit914), based on the probability of the current candidate word given theone or more history candidate words and the non-terminal class, textcorresponding to the speech input. Processing unit 908 is furtherconfigured to provide (e.g., with providing unit 916) an output based onthe text corresponding to the speech input.

In some examples, processing unit 908 is further configured to determine(e.g., with determining unit 914), using the neural network languagemodel, a second probability of the non-terminal class given the one ormore history candidate words. The virtual FST is composed using thesecond probability of the non-terminal class given the one or morehistory candidate words.

In some examples, processing unit 908 is further configured to traverse(e.g., with traversing unit 910) the user-specific language model FST,where a probability of the current candidate word among a plurality ofcandidate words represented in the user-specific language model FST isdetermined by traversing the user-specific language model FST. Thevirtual FST is composed using the probability of the current candidateword among the plurality of candidate words represented in theuser-specific language model FST.

In some examples, the one or more virtual states of the virtual FST arecomposed using phone-word units from the WFST and based on the currentcandidate word represented in the user-specific language model FST.

In some examples, processing unit 908 is further configured to, prior toreceiving the speech input, receive (e.g., with receiving unit 920 andvia input unit 906) user-specific data and generate (e.g., withgenerating unit 922) the user-specific language model FST using theuser-specific data.

In some examples, only one arc transitions out of each virtual state ofthe one or more virtual states of the virtual FST.

In some examples, processing unit 908 is further configured to compose(e.g., with composing unit 912) a second virtual FST using a secondlanguage model and the user-specific language model FST, and based onthe sequence of states and arcs of the WFST, where one or more virtualstates of the second virtual FST represent the current candidate wordcorresponding to the non-terminal class. Processing unit 908 is furtherconfigured to traverse (e.g., with traversing unit 910) the one or morevirtual states of the second virtual FST, where a second probability ofthe current candidate word given the one or more history candidate wordsand the non-terminal class is determined by traversing the one or morevirtual states of the second virtual FST. The text corresponding to thespeech input is determined based on the second probability of thecurrent candidate word given the one or more history candidate words andthe non-terminal class.

In some examples, the second language model is an n-gram language model.In some examples, the second language model is a second neural networklanguage model.

In some examples, processing unit 908 is further configured tointerpolate (e.g., with interpolating unit 918) 1) the probability ofthe current candidate word given the one or more history candidate wordsand the non-terminal class and 2) the second probability of the currentcandidate word given the one or more history candidate words and thenon-terminal class. A combined probability of the current candidate wordgiven the one or more history candidate words and the non-terminal classis obtained by the interpolating. The text corresponding to the speechinput is determined based on the combined probability of the currentcandidate word given the one or more history candidate words and thenon-terminal class.

In some examples, the virtual FST is composed after traversing thesequence of states and arcs of the WFST.

In some examples, processing unit 908 is further configured to compose(e.g., with composing unit 912) the negating FST with the WFST prior totraversing the negating FST.

In some examples, the WFST is a static finite state transducer builtprior to receiving the speech input. In some examples, the negating FSTis a static finite state transducer built prior to receiving the speechinput. In some examples, the WFST is a single finite state transducercomposed from a Hidden Markov Model (HMM) topology, a context dependentphonetic model, a lexicon, and a language model. In some examples, thelanguage model is a unigram language model or a bigram language model.In some examples, the neural network language model is more accuratethan the language model. In some examples, the neural network languagemodel is a feedforward neural network language model. In some examples,the neural network language model is a recurrent neural network languagemodel.

The operation described above with respect to FIGS. 7A-B and 8A-C are,optionally, implemented by components depicted in FIGS. 1A-B, 3, 5, and9. For example, receiving operations (702, 806) can be implemented bymicrophone 113, audio circuitry 110, and/or processor(s) 120. Traversingoperations (704, 708, 714, 722, 808, 812, 816, 820), composingoperations (706, 712, 720, 810, 818, 826), determining operations (710,716, 814, 822), interpolating operations (724, 830), receiving operation(802), and generating operation 804 can be implemented by automaticspeech recognition module 500. It would be clear to a person of ordinaryskill in the art how other processes can be implemented based on thecomponents depicted in FIGS. 1A-B, 3, 5, and 9.

It is understood by persons of skill in the art that the functionalblocks described in FIG. 9 are, optionally, combined or separated intosub-blocks to implement the principles of the various describedembodiments. Therefore, the description herein optionally supports anypossible combination or separation or further definition of thefunctional blocks described herein. For example, processing unit 908 canhave an associated “controller” unit that is operatively coupled withprocessing unit 908 to enable operation. This controller unit is notseparately illustrated in FIG. 9 but is understood to be within thegrasp of one of ordinary skill in the art who is designing a devicehaving a processing unit 908, such as device 900. As another example,one or more units, such as audio receiving unit 904, may be hardwareunits outside of processing unit 908 in some embodiments. Thedescription herein thus optionally supports combination, separation,and/or further definition of the functional blocks described herein.

In accordance with some implementations, a computer-readable storagemedium (e.g., a non-transitory computer readable storage medium) isprovided, the computer-readable storage medium storing one or moreprograms for execution by one or more processors of an electronicdevice, the one or more programs including instructions for performingany of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., aportable electronic device) is provided that comprises means forperforming any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., aportable electronic device) is provided that comprises a processing unitconfigured to perform any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., aportable electronic device) is provided that comprises one or moreprocessors and memory storing one or more programs for execution by theone or more processors, the one or more programs including instructionsfor performing any of the methods or processes described herein.

Although the above description uses terms first, second, etc. todescribe various elements, these elements should not be limited by theterms. These terms are only used to distinguish one element fromanother. For example, a first probability could be termed a secondprobability, and, similarly, a second probability could be termed afirst probability, without departing from the scope of the presentinvention. The first probability and the second probability are bothprobabilities, but they are not the same probability.

The terminology used in the description of the various describedembodiments herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used in thedescription of the various described embodiments and the appendedclaims, the singular forms “a”, “an,” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will also be understood that the term “and/or” as usedherein refers to and encompasses any and all possible combinations ofone or more of the associated listed items. It will be furtherunderstood that the terms “includes,” “including,” “comprises,” and/or“comprising,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

The term “if” may be construed to mean “when” or “upon” or “in responseto determining” or “in response to detecting,” depending on the context.Similarly, the phrase “if it is determined” or “if [a stated conditionor event] is detected” may be construed to mean “upon determining” or“in response to determining” or “in accordance with determining” or“upon detecting [the stated condition or event]” or “in response todetecting [the stated condition or event],” depending on the context.

As described above, one aspect of the present technology is thegathering and use of data available from various sources to generatelanguage models and thus improve the accuracy of speech recognition. Thepresent disclosure contemplates that in some instances, this gathereddata may include personal information data that uniquely identifies orcan be used to contact or locate a specific person. Such personalinformation data can include demographic data, location-based data,telephone numbers, email addresses, home addresses, or any otheridentifying information.

The present disclosure recognizes that the use of such personalinformation data, in the present technology, can be used to the benefitof users. For example, the personal information data can be used togenerate user-specific language models. Accordingly, use of suchpersonal information data enables more accurate speech recognition.Further, other uses for personal information data that benefit the userare also contemplated by the present disclosure.

The present disclosure further contemplates that the entitiesresponsible for the collection, analysis, disclosure, transfer, storage,or other use of such personal information data will comply withwell-established privacy policies and/or privacy practices. Inparticular, such entities should implement and consistently use privacypolicies and practices that are generally recognized as meeting orexceeding industry or governmental requirements for maintaining personalinformation data private and secure. For example, personal informationfrom users should be collected for legitimate and reasonable uses of theentity and not shared or sold outside of those legitimate uses. Further,such collection should occur only after receiving the informed consentof the users. Additionally, such entities would take any needed stepsfor safeguarding and securing access to such personal information dataand ensuring that others with access to the personal information dataadhere to their privacy policies and procedures. Further, such entitiescan subject themselves to evaluation by third parties to certify theiradherence to widely accepted privacy policies and practices.

Despite the foregoing, the present disclosure also contemplatesembodiments in which users selectively block the use of, or access to,personal information data. That is, the present disclosure contemplatesthat hardware and/or software elements can be provided to prevent orblock access to such personal information data. For example, in the caseof advertisement delivery services, the present technology can beconfigured to allow users to select to “opt in” or “opt out” ofparticipation in the collection of personal information data duringregistration for services. In another example, users can select not toprovide location information for targeted content delivery services. Inyet another example, users can select to not provide precise locationinformation, but permit the transfer of location zone information.

Therefore, although the present disclosure broadly covers use ofpersonal information data to implement one or more various disclosedembodiments, the present disclosure also contemplates that the variousembodiments can also be implemented without the need for accessing suchpersonal information data. That is, the various embodiments of thepresent technology are not rendered inoperable due to the lack of all ora portion of such personal information data. For example, content can beselected and delivered to users by inferring preferences based onnon-personal information data or a bare minimum amount of personalinformation, such as the content being requested by the deviceassociated with a user, other non-personal information available to thecontent delivery services, or publically available information.

Although the disclosure and examples have been fully described withreference to the accompanying figures, it is to be noted that variouschanges and modifications will become apparent to those skilled in theart. Such changes and modifications are to be understood as beingincluded within the scope of the disclosure and examples as defined bythe appended claims.

What is claimed is:
 1. A non-transitory computer-readable medium havinginstructions stored thereon, the instructions, when executed by one ormore processors, cause the one or more processors to: receive speechinput; traverse, based on the speech input, a sequence of states andarcs of a weighted finite state transducer (WFST), wherein: the sequenceof states and arcs represents one or more history candidate words and acurrent candidate word; and a first probability of the candidate wordgiven the one or more history candidate words is determined bytraversing the sequences of states and arcs of the WFST; traverse anegating finite state transducer (FST), wherein traversing the negatingFST negates the first probability of the candidate word given the one ormore history candidate words; compose a virtual FST using a neuralnetwork language model and based on the sequence of states and arcs ofthe WFST, wherein one or more virtual states of the virtual FSTrepresent the current candidate word; traverse the one or more virtualstates of the virtual FST, wherein a second probability of the candidateword given the one or more history candidate words is determined bytraversing the one or more virtual states of the virtual FST; determine,based on the second probability of the candidate word given the one ormore history candidate words, text corresponding to the speech input;and provide an output based on the text corresponding to the speechinput.
 2. The non-transitory computer-readable medium of claim 1,wherein the virtual FST is composed after traversing the sequence ofstates and arcs of the WFST.
 3. The non-transitory computer-readablemedium of claim 1, wherein only one arc transitions out of each virtualstate of the one or more virtual states of the virtual FST.
 4. Thenon-transitory computer-readable medium of claim 1, wherein theinstructions further cause the one or more processors to: determine,using the neural network language model, a third probability of thecandidate word given the one or more history candidate words, whereinthe virtual FST is composed using the third probability of the candidateword given the one or more history candidate words.
 5. Thenon-transitory computer-readable medium of claim 1, wherein theinstructions further cause the one or more processors to: compose asecond virtual FST using a second language model and based on thesequence of states and arcs, wherein one or more virtual states of thesecond virtual FST represents the current candidate word; and traversethe one or more virtual states of the second virtual FST, wherein afourth probability of the candidate word given the one or more historycandidate words is determined by traversing the one or more virtualstates of the second virtual FST, and wherein the text corresponding tothe speech input is determined based on the fourth probability of thecandidate word given the one or more history candidate words.
 6. Thenon-transitory computer-readable medium of claim 5, wherein theinstructions further cause the one or more processors to: interpolatethe second probability of the candidate word given the one or morehistory candidate words and the fourth probability of the candidate wordgiven the one or more history candidate words, and wherein: a combinedprobability of the candidate word given the one or more historycandidate words is determined by the interpolating; and the textcorresponding to the speech input is determined based on the combinedprobability of the candidate word given the one or more historycandidate words.
 7. The non-transitory computer-readable medium of claim5, wherein the second language model is an n-gram language model.
 8. Thenon-transitory computer-readable medium of claim 1, wherein theinstructions further cause the one or more processors to: compose thenegating FST with the WFST prior to traversing the negating FST.
 9. Thenon-transitory computer-readable medium of claim 8, wherein the virtualFST is composed prior to traversing the one or more virtual states ofthe virtual FST.
 10. The non-transitory computer-readable medium ofclaim 1, wherein the WFST is a static finite state transducer builtprior to receiving the speech input.
 11. The non-transitorycomputer-readable medium of claim 1, wherein the negating FST is astatic finite state transducer built prior to receiving the speechinput.
 12. The non-transitory computer-readable medium of claim 1,wherein the WFST is a single finite state transducer composed from aHidden Markov Model (HMM) topology, a context dependent phonetic model,a lexicon, and a language model.
 13. The non-transitorycomputer-readable medium of claim 12, wherein the language model is aunigram language model or a bigram language model.
 14. A non-transitorycomputer-readable medium having instructions stored thereon, theinstructions, when executed by one or more processors, cause the one ormore processors to: receive speech input; traverse, based on the speechinput, a sequence of states and arcs of a weighted finite statetransducer (WFST), wherein: the sequence of states and arcs representsone or more history candidate words and a non-terminal class; and afirst probability of the non-terminal class given the one or morehistory candidate words is determined by traversing the sequences ofstates and arcs of the WFST; traverse a negating finite state transducer(FST), wherein traversing the negating FST negates the first probabilityof the non-terminal class given the one or more history candidate words;compose a virtual FST using a neural network language model and auser-specific language model FST, and based on the sequence of statesand arcs of the WFST, wherein one or more virtual states of the virtualFST represent a current candidate word corresponding to the non-terminalclass; traverse the one or more virtual states of the virtual FST,wherein a probability of the current candidate word given the one ormore history candidate words and the non-terminal class is determined bytraversing the one or more virtual states of the virtual FST; determine,based on the probability of the current candidate word given the one ormore history candidate words and the non-terminal class, textcorresponding to the speech input; and provide an output based on thetext corresponding to the speech input.
 15. The non-transitorycomputer-readable medium of claim 14, wherein the instructions furthercause the one or more processors to: determine, using the neural networklanguage model, a second probability of the non-terminal class given theone or more history candidate words, wherein the virtual FST is composedusing the second probability of the non-terminal class given the one ormore history candidate words.
 16. The non-transitory computer-readablemedium of claim 14, wherein the instructions further cause the one ormore processors to: traverse the user-specific language model FST,wherein a probability of the current candidate word among a plurality ofcandidate words represented in the user-specific language model FST isdetermined by traversing the user-specific language model FST, andwherein the virtual FST is composed using the probability of the currentcandidate word among the plurality of candidate words represented in theuser-specific language model FST.
 17. The non-transitorycomputer-readable medium of claim 14, wherein the one or more virtualstates of the virtual FST are composed using phone-word units from theWFST and based on the current candidate word represented in theuser-specific language model FST.
 18. The non-transitorycomputer-readable medium of claim 14, wherein the instructions furthercause the one or more processors to: prior to receiving the speechinput: receive user-specific data; and generate the user-specificlanguage model FST using the user-specific data.
 19. A method forperforming speech-to-text conversion, the method comprising: at anelectronic device having a processor and memory: receiving speech input;traversing, based on the speech input, a sequence of states and arcs ofa weighted finite state transducer (WFST), wherein: the sequence ofstates and arcs represents one or more history candidate words and acurrent candidate word; and a first probability of the candidate wordgiven the one or more history candidate words is determined bytraversing the sequences of states and arcs of the WFST; traversing anegating finite state transducer (FST), wherein traversing the negatingFST negates the first probability of the candidate word given the one ormore history candidate words; composing a virtual FST using a neuralnetwork language model and based on the sequence of states and arcs ofthe WFST, wherein one or more virtual states of the virtual FSTrepresent the current candidate word; traversing the one or more virtualstates of the virtual FST, wherein a second probability of the candidateword given the one or more history candidate words is determined bytraversing the one or more virtual states of the virtual FST;determining, based on the second probability of the candidate word giventhe one or more history candidate words, text corresponding to thespeech input; and providing an output based on the text corresponding tothe speech input.
 20. An electronic device comprising: one or moreprocessors; and memory having instructions stored thereon, theinstructions, when executed by the one or more processors, cause the oneor more processors to: receive speech input; traverse, based on thespeech input, a sequence of states and arcs of a weighted finite statetransducer (WFST), wherein: the sequence of states and arcs representsone or more history candidate words and a current candidate word; and afirst probability of the candidate word given the one or more historycandidate words is determined by traversing the sequences of states andarcs of the WFST; traverse a negating finite state transducer (FST),wherein traversing the negating FST negates the first probability of thecandidate word given the one or more history candidate words; compose avirtual FST using a neural network language model and based on thesequence of states and arcs of the WFST, wherein one or more virtualstates of the virtual FST represent the current candidate word; traversethe one or more virtual states of the virtual FST, wherein a secondprobability of the candidate word given the one or more historycandidate words is determined by traversing the one or more virtualstates of the virtual FST; determine, based on the second probability ofthe candidate word given the one or more history candidate words, textcorresponding to the speech input; and provide an output based on thetext corresponding to the speech input.